U.S. patent application number 17/085808 was filed with the patent office on 2021-02-18 for method and system for combining physiological and machine information to enhance function.
The applicant listed for this patent is Resonea, Inc.. Invention is credited to Sanjiv M. Narayan, Ruchir Sehra.
Application Number | 20210045678 17/085808 |
Document ID | / |
Family ID | 1000005181637 |
Filed Date | 2021-02-18 |
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United States Patent
Application |
20210045678 |
Kind Code |
A1 |
Narayan; Sanjiv M. ; et
al. |
February 18, 2021 |
METHOD AND SYSTEM FOR COMBINING PHYSIOLOGICAL AND MACHINE
INFORMATION TO ENHANCE FUNCTION
Abstract
The present invention relates generally and specifically to
combining biological sensors with external machines using machine
learning to form computerized representations that can control
effectors to deliver therapy or enhance performance.
Inventors: |
Narayan; Sanjiv M.; (Palo
Alto, CA) ; Sehra; Ruchir; (Scottsdale, AZ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Resonea, Inc. |
Scottsdale |
AZ |
US |
|
|
Family ID: |
1000005181637 |
Appl. No.: |
17/085808 |
Filed: |
October 30, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16131651 |
Sep 14, 2018 |
10849552 |
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17085808 |
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15443888 |
Feb 27, 2017 |
10092235 |
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16131651 |
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PCT/US2015/046819 |
Aug 25, 2015 |
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15443888 |
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PCT/US2015/047820 |
Aug 31, 2015 |
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15443888 |
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PCT/US2015/046819 |
Aug 25, 2015 |
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PCT/US2015/047820 |
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62043760 |
Aug 29, 2014 |
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62043760 |
Aug 29, 2014 |
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62043760 |
Aug 29, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/7267 20130101;
A61B 5/01 20130101; A61B 5/0816 20130101; A61B 5/024 20130101; A61B
5/40 20130101; A61B 5/25 20210101; A61F 2/72 20130101; A61B 5/4836
20130101; A61B 5/14539 20130101; A61B 5/0205 20130101; A61B
2560/0242 20130101; A61B 5/0531 20130101; A61B 5/389 20210101; A61B
5/318 20210101; A61B 5/296 20210101; A61B 5/369 20210101; A61B
5/6877 20130101; A61B 5/24 20210101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/04 20060101 A61B005/04; A61F 2/72 20060101
A61F002/72; A61B 5/01 20060101 A61B005/01; A61B 5/0205 20060101
A61B005/0205; A61B 5/024 20060101 A61B005/024; A61B 5/0402 20060101
A61B005/0402; A61B 5/0476 20060101 A61B005/0476; A61B 5/0488
20060101 A61B005/0488; A61B 5/053 20060101 A61B005/053; A61B 5/08
20060101 A61B005/08; A61B 5/145 20060101 A61B005/145; A61B 5/0408
20060101 A61B005/0408; A61B 5/0492 20060101 A61B005/0492 |
Claims
1. A method for enhancing mental alertness in an individual, the
method comprising: detecting signals of normal and abnormal
function of mental alertness of an individual at one or more
sensors, wherein the sensors measure signals of breathing;
mathematically processing said signals using an enciphered
functional network to create a symbolic representation of the
mental alertness of the individual; and delivering effector
responses based on the symbolic representation in order to enhance
or reinstate the mental alertness.
2. The method of claim 1, wherein the detection is performed
without requiring knowledge of the precise physiological mapping of
the signals or without requiring use of signals empirically
associated with alertness.
3. The method of claim 1, wherein the sensed signals include one or
more signals from blood vessel flow, vasomotor activity, skin
electrical activity, heart rate or heart rate variations, breathing
rate and cellular edema.
4. The method of claim 1, wherein the effector response is tailored
to the individual based on the symbolic representation of mental
alertness.
5. The method of claim 1, wherein the effector response includes
biofeedback to enhance mental alertness.
6. The method of claim 1, wherein the effector response includes
delivering energy attenuated to the individual's symbolic
representation of mental alertness.
7. The method of claim 6, wherein energy modulates the mental
alertness to a normal or enhanced state based on the symbolic
representation created by the enciphered functional network.
8. The method of claim 7, wherein the energy includes stimulation
to the scalp.
9. The method of claim 6, wherein the effector response includes
audio stimulation.
10. The method of claim 1, wherein the mental alertness enhancement
is demonstrated by measurable improvement in a task.
11. A method of detecting mental alertness and/or fatigue in an
individual comprising: detecting signals of normal and abnormal
function of mental alertness of an individual at one or more
sensors, wherein the sensors measure signals of breathing;
mathematically processing said signals using an enciphered
functional network to create a symbolic representation of the
mental alertness and/or mental fatigue of the individual; and based
on the symbolic representation, delivering an effector response
sufficient to modify the individuals mental alertness and/or mental
fatigue.
12. The method of claim 11, wherein the detection is performed
without requiring knowledge of the precise physiological mapping of
the signals or without requiring use of signals empirically
associated with alertness.
13. The method of claim 11, wherein the sensed signals include one
or more signals from blood vessel flow, vasomotor activity, skin
electrical activity, heart rate or heart rate variations, breathing
rate and cellular edema.
14. The method of claim 11, wherein the effector response includes
biofeedback to enhance mental alertness.
15. The method of claim 14, wherein the effector response includes
delivering energy attenuated to the individual's symbolic
representation of mental alertness.
16. The method of claim 15, wherein energy modulates the mental
alertness to a normal or enhanced state based on the symbolic
representation created by the enciphered functional network.
17. The method of claim 11 The method of claim 1, wherein the
mental alertness enhancement is demonstrated by measurable
improvement in a task.
18. A method for affecting the performance of a task by enhancing
mental alertness of an individual, the method comprising: detecting
signals of normal and abnormal function of said mental alertness of
an individual at one or more sensors, wherein the sensors measure
signals of breathing, and wherein said detection is performed
without requiring knowledge of the precise physiological mapping of
the signals or without requiring use of signals empirically
associated with alertness; mathematically processing said signals
to create a symbolic representation of the mental alertness of the
individual; and delivering effector responses based on the symbolic
representation in order to enhance or reinstate the mental
alertness, wherein said enhanced mental alertness is measured by
performance of the task.
19. The method of claim 18, wherein the sensed signals further
include one or more of blood vessel flow, vasomotor reactivity,
skin electrical conductivity, heart rate or heart rate variations,
breathing rate, and cellular edema.
20. The method of claim 18, wherein the effector response comprises
biofeedback to enhance mental alertness.
21. The method of claim 20 wherein the biofeedback in in the form
of electrical energy to stimulate said individual.
22. The method of claim 18, wherein the effector response comprises
audio stimulation.
23. The method of claim 18, wherein the method includes an
enciphered network that can be trained through machine
learning.
24. The method of claim 18, wherein the effector response comprises
energy emanating from a device external to the body.
25. A method for detecting mental fatigue in an individual as
measured by performance of a task, the method comprising: detecting
abnormal and normal breathing signals from the body using sensors,
said detection not requiring the precise physiological mapping of
the signals or detection of signals empirically associated with
fatigue; mathematically processing said signals to create a
symbolic representation of the fatigue of the individual; and
delivering effector responses based on the symbolic representation
in order to reduce fatigue, wherein said reduced fatigue is
measured by enhanced performance of the task.
26. The method of claim 25, wherein the sensed signals further
include one or more of blood vessel flow, vasomotor reactivity,
skin electrical conductivity, heart rate or heart rate variations,
breathing rate, and cellular edema.
27. The method of claim 25, wherein the effector response comprises
biofeedback to enhance mental alertness.
28. The method of claim 27, wherein the biofeedback in in the form
of electrical energy to stimulate said individual.
29. The method of claim 25, wherein the effector response comprises
audio stimulation.
30. The method of claim 25, wherein the method includes an
enciphered network that can be trained through machine
learning.
31. The method of claim 25, wherein the effector response comprises
energy emanating from a device external to the body.
Description
[0001] This application is a continuation of U.S. Ser. No.
16/131,651, filed Sep. 14, 2018, currently pending, which in turn
is a continuation of U.S. Ser. No. 15/443,888, filed Feb. 27, 2017,
now U.S. Pat. No. 10,092,235, issued Oct. 9, 2018, which in turn is
a continuation of International Application No. PCT/US2015/046819,
filed Aug. 25, 2015, which in turn claims priority to U.S.
Provisional Application No. 62/043,760, filed Aug. 29, 2014, the
entire contents of which are incorporated by reference in their
entirety; U.S. Ser. No. 15/443,888, filed Feb. 27, 2017, now U.S.
Pat. No. 10,092,235, issued Oct. 9, 2018 is also a
continuation-in-part of International Application No.
PCT/US2015/047820, filed Aug. 31, 2015, which in turn claims
priority to U.S. Provisional Application No. 62/043,760, filed Aug.
29, 2014; International Application No. PCT/US2015/047820 is also a
continuation-in-part of International Application No.
PCT/US2015/046819, filed Aug. 25, 2015, which in turn claims
priority to U.S. Provisional Application No. 62/043,760, filed Aug.
29, 2014, the entire contents of which are incorporated by
reference in their entirety. The entire contents of each of these
priority documents (U.S. Ser. Nos. 16/131,651 and 15/443,888 and
62/043,760 and International Application Nos. PCT/US2015/046819 and
PCT/US2015/047820) is incorporated herein by reference in their
entirety.
FIELD
[0002] The present invention relates generally and specifically to
combining biological sensors with external machines using machine
learning to form computerized representations capable of
controlling effectors to deliver therapy or enhance performance.
The invention integrates sensed signals from body systems and
artificial devices with outputs from measureable body systems and
artificial devices to create learned networks. Measurable body
systems include the central and peripheral nervous systems,
cardiovascular system, respiratory system, skeletal muscles and
skin well as any other body systems that are capable of producing
measurable signals. Artificial devices include diagnostic sensors,
medical stimulating or prosthetic devices and/or non-medical
systems. The invention has applications in sleep and wakefulness,
sleep-disordered breathing, memory and cognition, monitoring and
responding to obesity or heart failure and other conditions, or
more generally in enhancing performance via external devices. This
disclosure outlines several applications of this invention, using
as an example methods and systems to enhance sleep-related bodily
functions for use in normal individuals or patients with
sleep-breathing disorders.
[0003] The present application also relates generally to functions
of the nervous system of the body. More specifically, the present
application is directed to a method, system and apparatus to modify
or enhance nervous control of the body for use in normal
individuals or patients with disease.
[0004] This application incorporates by reference the entire
subject matter and application of attorney docket #2480-2 PCT
(application PCT/US15/46819, filed Aug. 25, 2015); and attorney
docket number 2480-3 PCT (Application No. PCT/US2015/047820, filed
Aug. 31, 2015).
BRIEF DISCUSSION OF RELATED ART
[0005] Several functions of the body are mediated by the brain
(central nervous system) and/or peripheral nervous system. These
functions include classical "neurological" functions such as vision
or hearing, but also nearly all activities of daily life, including
learning, moving, or operating machinery.
[0006] The human body has long been interfaced with artificial
devices or machines. Prosthetic limbs have for centuries been made
of wood combined with metal and other materials. Through recent
technological advances, devices often now have sophisticated
materials, design and control for a specific purpose--such as a
robotic limb (see for instance
http://singularityhub.com/2013/07/24/darpas-brain-controlled-prosthetic-a-
rm-and-a-bionic-hand-that-can-touch) or a glucose-sensing insulin
infusion pump.
[0007] In many situations, the body's ability to perform such
functions is constrained. Constraint can take many forms and may be
physical or functional. Physical constraints include an external
obstacle preventing movement of a limb in an enclosed space such as
may affect a warrior or scuba diver. A physical constraint may also
be internal, such as loss of a limb from amputation. Functional
constraints may include a classical disease, such as stroke that
prevents an individual's ability to move the foot. However,
functional constraints may also include underperformance on a task
due to insufficient training, knowledge or acquisition of skills,
or through disuse.
[0008] Many of these functions are mediated by the brain (central
nervous system) and/or peripheral nervous system. These functions
include classical "neurological" functions such as vision or
hearing, but also nearly all activities of daily life, including
learning, moving, or operating machinery.
[0009] Many attempts have been made to address some of these
constraints, using a familiar paradigm that body sensors (e.g. the
eye), nervous function (e.g. the central and peripheral nervous
system) and effector organs (e.g. a muscle group) can be often be
functionally mapped to specific anatomic locations.
[0010] Conscious and purposeful interventions are hence applied
when these functions are constrained, e.g. a soldier can use a
finger to activate a device if his/her foot cannot activate a pedal
due to an obstacle or, in an amputee, interfacing a robotic arm to
specific nerve fibers that formerly controlled the biological
arm.
[0011] What is lacking is how devices can be used to automatically
("intelligently") tailor therapy to restore lost physiological
function or enhance an existing function in a specific individual.
This inability for prior and current devices to automatically
tailor therapy and restore or enhance a function is striking when
examining how the human brain senses, integrates and controls
bodily functions.
[0012] However, the functional mapping required for classical
solutions to address constraints is complex. Such functional
mapping or `atlases` are debated even for some "simple sensations"
such as visual recognition of a face, and far less clear for
complex functions such as alertness. Much data has come from animal
models that are not well suited to model or analyze the
neuroanatomical basis of thought or complex human activity.
[0013] The prior art has extensively studied, yet imprecisely
defined, which regions of the brain control bodily functions and
how they interact with other physiological components in a network.
Simple bodily functions, such as moving the biceps of the left arm
or sensing from the right index finger, are well defined and often
conserved between individuals. Functional mapping or "atlases" are
debated even for some "simple sensations" such as visual
recognition of a face. Moreover, other bodily functions including
"higher cortical" functions are neither well defined nor conserved.
This includes sleep, cognition, memory, mood, alertness,
sensory-motor and many other activities.
[0014] Currently, machines that interface with, augment or aid
human function are largely predicated on a detailed knowledge of
neuroimaging, cortical mapping and even peripheral nerve mapping of
documented normal and abnormal functions/pathways. Such machines
have largely attempted to replace, improve or reinstate function
based upon the normal documented functions and pathways.
[0015] Currently, machines that modulate bodily function are
largely based on a precise detailed knowledge of physiology, which
for the brain would include neuroimaging, brain mapping and
peripheral nerve mapping of normal and abnormal functions.
Unfortunately, such detailed knowledge is often incomplete. Mapping
of functional locations often vary between individuals--and even in
the same person at different times. Many functions are poorly
mapped, such as memory, cognition and mental performance. Even for
well mapped functions, studies that define physiological function
often raise additional uncertainties in this functionality.
[0016] Conversely, it would be of immense benefit to society to
construct a device able to restore/enhance such human
functionality, i.e. to compensate for the constraints alluded to
above, without the need to define or replicate precise neural
pathways for device interfacing, or without the need to consciously
alter the function (e.g. training to use a finger instead of a foot
to operate a pedal). Currently, there are few methods in the prior
art to achieve this goal. Such devices could be used to enhance
performance in individuals without disease, or restore lost
function in those with disease.
[0017] Mapping functional domains of a bodily function--the network
of physiological systems associated with that function including
sensed signals and biological effectors that control it--is
difficult. Mapping of functional domains is particularly difficult
for functions involving the brain. However, there is an urgent need
to sense and modulate functional domains whose altered function may
cause disease or suboptimal performance.
[0018] In traditional theory, sleep and wakefulness are modulated
by brain regions including the posterior hypothalamus, while memory
is encoded by the hippocampus and other regions of the limbic
system. However, it is not clear what brain regions are responsible
for controlling sleep, or for mediating abnormal breathing in
central sleep apnea. Regions of the brainstem that control airway
muscles are better characterized, such as nuclei in the medulla
oblongata for the hypoglossal nerve (twelfth cranial nerve) that
controls tongue movement. Yet, how nuclei are integrated into
abnormal breathing to produce obstructive sleep apnea is not
understood. As a result, it has been difficult to treat this
condition even using novel systems that activate tongue motion to
reduce obstruction.
[0019] Sleep is a bodily function that integrates the nervous
system, skeletal muscle, cardiopulmonary, and other body systems.
Sleep alternates with and enables subsequent wakefulness, and is
required for normal functioning of most organ systems. Sleep is
traditionally considered to be controlled by specific regions of
the brainstem (primitive brain) that both regulate and are
modulated by function of the higher brain (cerebral cortex),
muscles controlling breathing, other involuntary muscles such as
sphincters of the gastrointestinal and genitourinary tracts,
voluntary muscles such as muscles of the legs or arms, sensory
function, and other bodily functions.
[0020] Much work over several decades has strived to define which
regions of the brain subtend bodily functions such as sleep. As
outlined above, while functional mapping is well defined for
"simple" functions such as controlling a defined muscle (e.g., the
biceps of the upper arm) or sensation (e.g., the right index
finger), it is far less clear for sleep. Interactions between the
multiple organ systems impacted by sleep further complicate precise
mapping.
[0021] An individual's ability to sleep may be compromised in many
ways. Among the most important and common are sleep hypopnea
(reduced breathing) and apnea (absence of breathing), in which
impaired breathing in sleep interrupts sleep functioning, and
primary sleep disorders such as insomnia, where the individual
cannot sleep efficiently or sufficiently. All sleep disorders
negatively impact wakefulness, producing daytime drowsiness that
impairs daily activities. Sleep disorders can also lead to
disorders from breathing such as low oxygen levels with metabolic
effects including acidosis, disorders of the heart such as failure
and abnormal rhythms, disorders of the immune system causing
susceptibility to infection, psychological disorders such as
stress, depression, anxiousness and psychosis, and several other
states of poor functioning and disease.
[0022] Sleep apnea may be obstructive or central. Obstructive sleep
apnea is increasingly recognized in individuals who snore, who are
overweight and who may develop sequelae such as heart failure.
Central sleep apnea is also common, yet is under-recognized and
associated with comorbidities such as heart failure. It is likely
that central sleep apnea also occurs alongside obstructive sleep
apnea, since treatments that physically open the throat muscles and
prevent obstruction may sometimes leave residual apnea.
[0023] Obstructive sleep apnea (OSA) results from complete or
partial airway collapse in sleep. Conversely, central sleep apnea
(CSA) results from reduced brain stimulation of the respiratory
muscles in sleep. Both forms are typically diagnosed using
overnight polysomnography (PSG), a test that measures at least
eight (8) channels including the electroencephalogram (EEG),
electrooculogram (EOG), electrocardiogram (ECG), chin
electromyogram (EMG), airflow, respiratory "effort," oxygen
saturation (SaO.sub.2 or sat), and body position. However, this is
a cumbersome test typically performed with an overnight hospital
stay attended by physicians, is not well liked by patients, cannot
easily be repeated to assess the impact of therapy and is difficult
to perform at home. Recent studies have shown that commercial tests
offered to circumvent traditional polysomnography are suboptimal at
best.
[0024] From a polysomnogram, apnea is defined as absence of
breathing (no tidal volume) for at least 10 seconds, while hypopnea
is defined as decrease in tidal volume of 30% for at least 10
seconds accompanied by at least a 3% decrease in oxygen saturation
or terminated by arousal from sleep. Apnea is defined as
obstructive if accompanied by inspiratory effort against the
occluded pharynx. Without such accompanying effort, apnea is
defined as central. Similarly, hypopnea is obstructive if there are
signs of upper airway flow limitation, and is otherwise considered
central. The apnea-hypopnea index (AHI) is the ratio of
apnea-to-hypopnea per hour of sleep, and is classified as no sleep
apnea (AHI <5), mild sleep apnea (AHI of 5-15), moderate sleep
apnea (AHI of 15-30) or severe sleep apnea (AHI >30).
[0025] Several treatments are available for obstructive sleep
apnea, but these are often not well tolerated. The most commonly
used treatment currently is continuous positive airway pressure to
keep the airway open and reduce/eliminate obstruction. Other
options include mechanical splints and even surgical procedures to
reduce/eliminate obstruction. Some recent devices have applied
stimulation to the muscles of the tongue or face to eliminate
obstruction, but it is unclear how well they will work in the broad
population.
[0026] Few strategies have been proposed to improve central sleep
apnea--or more generally the central control of sleep. Since
central sleep apnea may relate directly to sleep disorders,
treatments for central sleep apnea may potentially also help other
conditions. It is increasingly appreciated that central sleep apnea
makes heart failure worse, and so treatment for central sleep apnea
may improve symptoms of heart failure, and other cardiac and
non-cardiac conditions such as insomnia and psychological
sequelae.
[0027] Pharmacological drug therapy is often used to induce sleep,
but these agents are not useful in sleep apnea. These drugs rarely
mimic the natural stages of sleep, rarely induce rapid eye movement
(REM) sleep that is essential for restfulness, and may
paradoxically worsen sleep disorders and produce daytime drowsiness
despite nighttime unconsciousness.
[0028] New therapeutic modalities are clearly needed to modulate
the complex functions outlined above--often including a component
of central or peripheral nervous system involvement. Emerging
modalities involve electrical stimulation/modulation of brain or
nervous system activity, typically at a specific target region. All
these current modalities suffer from a significant common problem,
as they attempt to perform therapy with no or minimal sensory
input, feedback, or modulation of such therapy based upon the
individual patient's neurological activity.
[0029] One example of electrical stimulation therapy is noninvasive
or minimally invasive trigeminal nerve stimulation (e.g.,
NEUROSIGMA.RTM.) that is being evaluated to treat depression and
seizures. Unfortunately, the true mechanism of action of such
therapy is unclear. Whether this is due to the actual trigeminal
nerve being stimulated, direct stimulation of the frontal lobe of
the brain, indirect inhibition of cerebral blood flow or some other
as yet unknown mechanism, still remains to be determined and will
affect the ability of such therapy to be applied successfully.
Additionally, this therapy is applied as a "one-size-fits-all"
approach without any adaptation for individual patient
responses.
[0030] Other non-invasive neuromodulation/stimulation approaches
are also being considered include stimulation of the vagus nerve
for seizures (Carbomed, Inc.). Similar to trigeminal nerve
stimulation, the mechanism is poorly understood, the actual
stimulation of the vagus nerve is unclear via this noninvasive
approach, and there is no individual patient adaptation. A number
of technologies are attempting to treat depression via noninvasive
transcranial application of an electrical and/or magnetic field
(Neuronetics Inc., Neosync Inc., Brainsway Inc., Cervel Neurotech
Inc., and Tal Medical Inc.). All of these approaches, even though
they show interesting preliminary data suffer, from the same
problems as above, namely, poor understanding of mechanism and lack
of patient-tailored therapy due to a lack of feedback and
adaptation for individual patients.
[0031] For apnea specifically, approaches that try to modulate
obstructive sleep apnea, including stimulation of the hypoglossal
nerve (Inspire Med Inc.) or other throat muscle (Apnex Medical
Inc.)--are being evaluated but typically do not have individual
patient-tailored therapies. In fact, whether direct management of
the obstruction resolves the problem of apnea is also unclear due
to commonality of a central sleep apnea component in most
patients.
[0032] Invasive approaches to neuromodulation include vagal nerve
stimulation to treat seizures and depression (Cyberonics), spinal
cord stimulation to treat pain (such as Medtronic Inc., Boston
Scientific Inc., Advanced Neuromed Inc.), direct deep brain
stimulation to treat seizures (Medtronic Inc., Boston Scientific
Inc.) or even cognitive disorders (Thync Inc.). However, these
therapies target single components of the physiologic network for a
bodily function, and are limited because they do not consider the
remaining network. This may lead to suboptimal therapy,
compensatory mechanisms that further diminish the efficacy of
therapy, or unwanted effects. Moreover, these therapies are only as
good as the accuracy of their specific targets, and brain/nerve
regions are imprecisely defined for many bodily functions including
sleep control, sleep-breathing conditions, cognition, alertness,
memory, overall mental performance, or response to obesity.
[0033] Traditional therapies have also not typically been effective
for managing central sleep apnea, other cognitive or performance
functions, alertness, heart failure or obesity.
[0034] When devices are used for other functions, such as the
increasing use of virtual environments, the goal is usually to
create an illusionary or representative environment by feeding
specific sensory inputs (primarily visual, tactile and/or auditory)
to replicate existing real-world experiences. Unfortunately, such
approaches may be limited in that normal pathways vary from
individual to individual. Thus, simulating normal often may not
accurately replicate that function for an individual nor represent
normal for that individual.
[0035] In other situations, the use of devices to enhance or
compensate for other functions such as motor tasks are limited or
constrained. Constraint can take many forms and may be physical or
functional. Physical constraints include an external obstacle
preventing movement of a limb in an enclosed space such as may
affect a warrior or scuba diver. A physical constraint may also be
internal, such as loss of a limb from amputation. Functional
constraints may include a classical disease, such as stroke that
prevents an individual's ability to move the foot. However,
functional constraints may also include underperformance on a task
due to insufficient training, knowledge or acquisition of skills,
or through disuse.
[0036] Many attempts have been made to address these constraints,
using a familiar paradigm that body sensors (e.g., the eye),
nervous function (e.g., the central and peripheral nervous system)
and effector organs (e.g., a muscle group) can often be
functionally mapped to specific anatomic locations. However, the
precise locations of the brain or other physiological systems that
control each task are not well defined. Such functional maps or
"atlases" are often debated for complex functions. Much data has
come from animal models that are not well suited to model or
analyze complex human functions or mental functions.
[0037] It would be of great benefit to society to develop a device
to enhance bodily functions by modulating its interfaced functional
components. For instance, a device to restore sleep functionality,
i.e., to prevent or treat central sleep disorders, would be of
great value. Such devices may improve daytime performance in
individuals without disease, or reduce symptoms in patients with
disorders associated with central sleep apnea such as heart
failure. It would also be of immense benefit to construct a device
able to restore/enhance other functions such as motor activity or
even some aspects of neural functioning without the need to define
precise physiological, neural or other pathways to guide therapy.
Currently, there are few methods in the prior art to achieve these
goals.
SUMMARY OF THE PRESENT INVENTION
[0038] One aspect of the invention is able to enhance performance
of function or re-instate (treat/replace) a lost function, even a
complex function, using sensors to detect signals naturally
associated with that function, engineering circuits based on
mathematical formation of a new symbolic code representing that
function, and a effector that can enhance or reinstate said
function. No a priori logic or programming is required to use the
invention. The invention uses the brain to figure out its own logic
and "piggy-back" on the brains ability to recognize patterns, in
other words, uniquely interfaces with existing brain functionality
in order to produce a desired result, e.g., re-task, enhance or
otherwise produced a desired function.
[0039] Because the nervous system also interacts with other systems
in the body, including the immune system and endocrine system, this
invention can be used to guide or interact with other organ systems
including destroying cancer cells, altering lung function or
altering a function of the gastrointestinal or genitourinary
tracts.
[0040] Thus, the current invention forms a symbolic internal
representation of simple and complex functions. This internal
representation is derived from empiric association between detected
signals and a function--e.g. electrical signals on the hand when a
finger touches an object, or scalp signals when an individual is
"alert" versus drowsy. This is actionable, yet more simple and a
more tractable computational task. It is akin to representing
something that is visualized by an "impressionist" painter rather
than one trained in the "realist" school. This approach is based
largely on the premise that in addition to the primary brain cortex
required for a task, that is difficult to precisely define,
secondary areas become activated and may be easily sensed and used
for training.
[0041] The current invention is based on the known observation that
under certain conditions of disease or training, cortical
plasticity is well described (e.g. DARPA limb projects, stroke
victims recovering function years later). Plasticity is also
observed in peripheral nerves, such that the dermatomal
distribution of a functioning peripheral nerve can expand when an
adjacent distribution is served by a diseased nerve. In other
words, the same function can now be served by different regions of
the central or peripheral nervous system. For the purposes to the
invention, the term "plasticity" means the ability of neurons to
adapt and change in response to a stimulus from the environment.
For example, the neural pathways and synapses may change in
response to changes in environment, behavior, emotions (moods), new
stimuli, thinking, neural processing injury and combinations
thereof. For example, a hat which cools the head, allows the hat to
be worn and include such hardware as sensing and stimulating
components which can interact with various and specific regions of
the head to achieve the desired result.
[0042] At a fundamental level, this `plasticity` does not require
knowledge of the precise underlying neurophysiological mapping. For
instance, in classical Pavlovian training, rats were taught to
salivate from non-food-related stimuli that were previously
associated with food during training. In other words, a new
stimulus--biological function can be programmed. The current
invention uses sophisticated sensors, mathematical approaches and
effector devices to do this in a deterministic fashion tailored to
a desired task.
[0043] The current invention does not focus on, nor rely upon, a
priori knowledge of known functions/pathways, that are often
complex and possibly undefined, but rather focuses on customized,
individualized solutions best suited to perform specific functions
through, potentially unused, capacity in pathways.
[0044] The current invention is further based on the concept that
the body can be considered a multipurpose computer, comprising
sensors, processing elements, and effector pathways/organs. It
should be possible to design a symbolic code to access or
"reprogram" this function to map specific sensors to specific
effectors throughout the body.
[0045] The current invention is further based on the concept that
the body has certain neural processing capacity, of which only a
minority is used even in highly stressful human activities such as
warrior combat (e.g. 40% capacity used). In highly focused,
non-life-or-death situations, a minority is still used, likely
20-40% e.g. NBA finals, SAT testing. Therefore there is substantial
residual capacity at any one time.
[0046] Tapping this capacity could improve performance, substitute
for a lost capability (e.g. amputee, stroke victim), replace an
external computer with an intrinsic biological computing function,
or retask processing representation of an existing function (e.g.
golf swing, noxious effect such as mild pain for incorrect
task/function).
[0047] Tapping this capacity/functionality could also be used to
recruit and reprogram certain (unused) portions of the body like a
multipurpose computer to perform a task e.g. controlling a remote
control unit, or bioencoding of information in the parietal lobe to
exploit the human brain's unparalleled ability for pattern
recognition.
[0048] Effector elements could include direct electrical outputs or
mechanical machines such as nerve stimulating electrodes or servo
motors to control a limb, digitized electronic signals such as
radiofrequency or infrared transmissions, or even virtualized data
such as avatars in a virtual world interface or elements in a large
database that can be queried.
[0049] Applications of these effector elements can be for
diagnostic purposes such as understanding different stimuli or body
functions (e.g. visual function, visual disease progression, mood,
alertness, detecting injury such as traumatic brain injury, cardiac
electrical and/or mechanical function, subclinical seizure
detection), learning about external world situations or
environments without subjecting the human body to discomfort (e.g.
sensing heat in a fire, detecting oxygen or toxic gas content in
the external environment such as a mine).
[0050] Effector devices or elements can be applied for medically
related therapy such as for brain related function (e.g. mood
disorders treated with brain stimulation, treating alertness in
patients with sleep disorders or central apnea, biofeedback for
stroke rehabilitation, deep brain stimulation for motion or seizure
disorders), other neurological diseases (e.g. notifying patients
with peripheral neuropathy of dangerous or noxious stimuli),
cardiac disease (e.g. arrhythmias treated with implanted devices,
cardiac function improved with mechanical or electrical devices),
or other organ disease modified with directed electrical or
mechanical elements.
[0051] Applications of these effector elements can be for training,
learning and performing of unusual physiological activities or
mechanical, non-physiologic functions. Examples of unusual
physiologic applications include enhancing learning or military and
civilian applications via transcranial direct current stimulation
(ref: www.scientificamerican.com/article/amping-up-brain-function),
improving athletic performance (e.g. noxious biofeedback for
incorrect motions/activities, pleasing brain stimulation for
correct motions/activities), enhancing sensory perceptions (e.g.
augmented visual sensors feeding facial recognition information via
lesser used pathways such as body propriosensors for use by
security forces, auditory sensors stimulating auditory pathways in
response to subthreshold or previously inaudible information),
performing typical tasks in non-typical ways either by overcoming
constraints or developing more efficient solutions (e.g. driving a
car with small finger movements or eye motion, analyzing big data
with motor movements such as scanning and arranging data with
virtual fingers and hands). Examples of mechanical functions
include operating a mechanical exoskeleton for soldiers, performing
tasks that are too difficult or dangerous for humans such as deep
sea exploration, armed combat, or even as basic as controlling
video games or remote controls.
[0052] Specific central or peripheral neural functions are
controlled by specific patterns of neuronal firing. A device can
mimic these patterns to effect the prior function--for instance,
stimulating a nerve using physiological patterns to control a
disused muscle. This functionality can also be applied without
direct knowledge or access to the primary muscle. Many functions
produce nerve activity at the body surface that may co-localized
e.g. dermatomal distributions of mixed peripheral nerves. One
example is sensation of the tip of shoulder blade at the "C234"
region, control of deltoid muscle function by the "C56" region, and
control of the diaphragm muscles and hence breathing at the "C345"
region. This can be performed empirically, without the direct need
for detailed neuroimaging studies.
[0053] The invention is also designed to monitor and modulate a
complex bodily function using a combined biological and machine
approach. Unlike the prior art, the current invention uses machine
learning to derive a robust relationship between sensed signatures
of measurable body systems and bodily functions in animals and in
particular human beings, but does not require presumed
physiological relationships or mechanisms. The invention is then
able to enhance performance of function or re-instate lost
functions using this robust relationship or enciphered functional
network.
[0054] For the purposes of this disclosure, the following
definitions apply.
[0055] Associative learning is defined as the process of linking
sensed signatures and other inputs with a body task. For this
disclosure, body tasks are typically complex tasks rather than
reflexive or other simple tasks. Associative learning may be
iterative, such that associations are modified ("learned") based
upon patterns of change between these processes. For the purposes
of this disclosure, this may be associating low impedance with
abnormal breathing.
[0056] Bodily function is defined as the processes needed to
perform a task, that may include physiologic or pathological
processes. Examples include sleep, sleep apnea, mental performance,
or the response to obesity. Bodily functions involve a network of
several functional domains that often interact including the brain
and central nervous system, peripheral nervous system,
cardiovascular, pulmonary, gastrointestinal, genitourinary, immune,
skin and other systems. A bodily function may result from
biological activity/function, and may involve a non-biological or
artificial component, e.g., reading with glasses, driving, using
remote control unit, a patient moving a combined natural/cybernetic
limb, etc.
[0057] Bodily signal means signals generated by and/or sensed from
a human, animal, plant, bacterial or other single-cell-based body
or multi-cell-based body. For purposes of this definition, viruses
and prions are included. Bodily signals particularly include
signals generated by and/or sensed from the human body. Bodily
signals are generally associated with bodily functions. The term
"non-bodily signal" indicates that it is generated from a source
other than a single- or multi-cell-based body. Examples include an
external "signal" from an external electrical source, machine,
sensor, etc. When the term "signal" is used without the term
"bodily" or "non-bodily", the term "signal" indicates that it
includes both "bodily signals" and "non-bodily" signals, i.e., it
includes all signals.
[0058] Body means the physical structure of a single-celled
organism, a multi-celled organism, viruses and prions. Organisms
include animals (such as, but not limited to, humans), plants,
bacteria, etc.
[0059] Effector is a means of performing a bodily task, and may
include a physical effector such as for moving a limb or moving the
diaphragm to enhance breathing during sleep, or an artificial
effector such as a cybernetic limb or electrical stimulation to
complete a task.
[0060] Effector response is the result of the effector, which may
or may not complete a bodily task. For instance, if the effector is
the triceps muscle in the arm, an effector response is to extend
the arm by 30 degrees, while the entire task may be to fully
straighten the arm.
[0061] Effector signal is the signal delivered by the invention to
the effector to produce the effector response.
[0062] Functional domain is the aggregation of all the elements
relating to a distinct bodily function, sometimes associated with a
specific organ system or a combination of systems that results in
the overall function, e.g., breathing. For a simple function, this
may reduce to a sensed "dermatomal distribution", for instance
sensation at the shoulder is mediated by sensory nerves from the
C435 distribution of the spinal cord. However, even such simple
domains are more complex (and networked), in that shoulder
sensation is mediated by nerves that also supply the heart.
Functional domains include nerves, blood vessels, the lymphatic
system, interstitial tissue planes and hormonal centers. Sensed
signatures are measurable physiological parameters or indices, used
individually or in combination from body systems above, that are
linked with a body function and in aggregate describe that
function.
[0063] Other definitions include a biological function, which means
any function that is the direct result of natural biological
activity such as breathing, heart beating, walking, running,
sleeping, dreaming and so on.
[0064] A symbolic model herein is a mathematical representation of
a function, linking measured sensed activity with a task even if
complete physiological description for that task are lacking. It
can also be termed a symbolic representation. This may include
analog recorded physiological signals, digital coded ciphers,
computer code, visual representations such as photographs or
graphics, and so on, and can be used to aid in rapid, clear
transformation to perform a specified method.
[0065] Associative learning is an iterative process of linking
processes, typically including sensed signatures and complex
biological tasks, and modifying these associations ("learning")
based upon patterns of change between these processes (for
instance, associating low impedance with abnormal breathing).
[0066] Enciphered network or enciphered functional network (EFN) is
defined as a model associating measured parameters (sensed
signatures) with aspects of the bodily task including effectors and
other sensors. This enables monitoring and improved functionality
of that body function. The enciphered network is designed to
parameterize a functional domain, for example it links sensed
activity with a task even if complete physiological or mechanistic
description for that task is lacking. This departs from the
traditional approach of meticulously mapping or recapitulating
functions in each biological organ system. The network can be
symbolic in the form of a symbolic representation such as symbolic
code, in which case it may be a mathematical or other abstract
representation. If applied to the nervous system, this can be
termed an enciphered nervous system.
[0067] Encipher is defined as the process of coding
information.
[0068] Enhanced performance or enhancement is defined as
improvement to the normal healthy and non-diseased baseline
function in an individual. Enhanced performance thus would not
include therapies for disease such as pacing in an individual with
abnormally slow heart rates or in a patient or an insulin pump in
known diabetic patients.
[0069] External machine is defined as a mechanical, electrical,
computational or other non-natural (native biological) device. This
may be external to the body but can be in contact with or implanted
within the body.
[0070] Extremity of the body is defined as limbs and associated
structures of the body including arms, legs, hands, feet, fingers,
toes, and subsegments thereof.
[0071] Functional domain is defined as the elements relating to a
bodily task. This may include sensed elements, analysis elements
and effector elements. Analysis elements may be "learned",
preprogrammed, reflexive, or passive. Each element may be
biological, non-biological or artificial.
[0072] For example, a functional domain may sometimes be associated
with a specific organ system or a combination of systems that
results in the overall function, e.g., breathing. For a simple
function, this may reduce to a sensed "dermatomal distribution",
for instance sensation at the shoulder is mediated by sensory
nerves from the C435 distribution of the spinal cord. However, even
simple domains may be more complex, in that shoulder sensation is
mediated by nerves that also supply the heart. Functional domains
thus include nerves, blood vessels, the lymphatic system,
interstitial tissue planes and hormonal organs. Sensed signatures
are measurable physiological parameters or indices from these organ
systems, used individually or in combination that are associated
with a body function and in aggregate describe that function.
[0073] Functionally associated is defined as sensed signals or
functional domains that occur together when that function occurs.
An example would be activity in portions of the brain controlling
breathing with activity in muscles of breathing such as the
intercostal muscles or diaphragm. Functional association does not
need to follow biological pathways. For example, a functional
association includes sensed activity in shoulder nerves with heart
related problems such as angina, in which shoulder nerve activity
is not part of the biological processes causing heart problems.
[0074] Machine learning is defined as a series of analytic methods
and algorithms that can learn from and make predictions on data by
building a model rather than following strictly static programming
instructions. These machine learning approaches "learn" patterns
and functions with at least some components that are not
preprogrammed (i.e., instructed). In this sense, machine learning
creates individualized solutions rather than generic ones. Machine
learning can take many forms, including artificial neural networks,
heuristics, deterministic rules and combined approaches.
[0075] Sensed signatures are defined as one or more signals from
sensors related to a bodily task. Sensors may be biological,
non-biological or artificial. Sensed signatures are inputs of the
functional domain. Sensed signatures can be physiological
parameters such as nerve firing rates and oxygenation level, that
are associated with the function in question such as sleep
disordered breathing.
[0076] Mental alertness is defined as an awake state that focuses
on a specific desired task, that can be measured by performance at
that task. Improved mental alertness is characterized by being
awake and performing mental and other tasks well. Reduced mental
alertness can include many states that include but are not limited
to impaired performance of a task, "mental fatigue", loss of focus,
attention deficit, somnolescence, sleepiness, narcolepsy, sleep and
disease processes that include the above as well as coma, "fugue"
state and others.
[0077] "Task" means a piece of work, action or movement to be done,
completed or undertaken. The term "bodily task" means a piece of
work, action or movement to be done, completed or undertaken by a
"body", defined herein.
[0078] Therapeutically effective is defined as an effector function
or dose of an intervention or therapy that produces measurable
improvement in one or more patient outcomes. An example would be
patterns of energy directed to the scalp to stimulate target
regions controlling breathing, in order to treat central sleep
apnea. Ideally, an intervention will minimize impact to other
regions of the body, in this case the scalp which may be achieved
by a small contact device rather than a cap that encompasses the
entire scalp, or focusing energy from a non-contact device on the
target region and not the entire head.
[0079] Other biological terms take their standard definitions, such
as heart failure, tidal volume, sleep apnea, obesity and so on.
[0080] This invention creates an enciphered functional network. The
potential number of uses of this invention are broad.
[0081] In one aspect, there is provided a method for interacting
with the nervous system, the method including detecting signals
associated with a biological function at one or more sensors,
processing said signals to create a representation, delivering
effector responses based on the symbolic representation, and
controlling a physical process.
[0082] In another aspect, there is provided a method to enhance
performance of a task, the method including detecting signals
associated with the task at one or more sensors, creating a
representation of said task, delivering effector responses to
modify said representation, and enhancing performance of said
task.
[0083] In another aspect, there is provided a method to treat a
disease, the method including detecting signals associated with
said disease at one or more sensors, creating a core symbolic
representation of said disease, stimulating a region of the body to
alter the representation between detected signals and the disease,
and treating the disease.
[0084] In another aspect, there is provided a method for
transforming sensed nerve activity, the method including detecting
signals associated with a biological function at one or more
sensors, processing said signals to create a representation,
delivering effector responses based on the representation, and
controlling a biological function.
[0085] In another aspect, there is provided a method for
controlling a device using biological signals, the method including
detecting biological signals from the body using one or more
sensors, converting detected biological signals from the sensor to
electronic representation, and outputting electronic information in
a recognizable format to electromechanically control a device.
[0086] In another aspect, there is provided a method to measure
visual function, the method including detecting biological signals
of biological sensory activation, processing these signals to
provide quantitative measures of sensation, creating a
representation of said sensory activation, and, optionally, using
the representation to determine optimal treatment.
[0087] In another aspect, there is provided a method for improving
specific human performance, the method including identifying
regions of the body associated with parts of the brain that serve a
specific function, placing low energy stimulating electrodes
proximate to said regions of the body, applying stimulation through
said electrodes to activate said parts of the brain, and measuring
changes in performance related to said parts of the brain.
[0088] In another aspect, there is provided a system for
interacting with the nervous system, the system including a
processor, a memory storing instructions that, when executed by the
processor, performs operations including detecting signals
associated with a biological function at one or more sensors,
processing said signals to create a representation, delivering
effector responses based on the symbolic representation, and
controlling a physical process.
[0089] In another aspect, there is provided a system to enhance
performance of a task, the system including a processor, a memory
storing instructions that, when executed by the processor, performs
operations including detecting signals associated with the task at
one or more sensors, creating a representation of said task,
delivering effector responses to modify said representation, and
enhancing performance of said task.
[0090] In another aspect, there is provided a system to treat a
disease, the system including a processor, a memory storing
instructions that, when executed by the processor, performs
operations includes detecting signals associated with said disease
at one or more sensors, creating a core symbolic representation of
said disease, stimulating a region of the body to alter the
representation between detected signals and the disease, and
treating the disease.
[0091] In another aspect, there is provided a system for
transforming sensed nerve activity, the system including a
processor, a memory storing instructions that, when executed by the
processor, performs operations including detecting signals
associated with a biological function at one or more sensors,
processing said signals to create a representation, delivering
effector responses based on the representation, and controlling a
biological function.
[0092] In another aspect, there is provided a system for
controlling a device using biological signals, the system including
a processor, a memory storing instructions that, when executed by
the processor, performs operations including detecting biological
signals from the body using one or more sensors, converting
detected biological signals from the sensor to electronic
representation, and outputting electronic information in a
recognizable format to electromechanically control a device.
[0093] In another aspect, there is provided a system to measure
visual function, the system including a processor, a memory storing
instructions that, when executed by the processor, performs
operations including detecting biological signals of biological
sensory activation, processing these signals to provide
quantitative measures of sensation, creating a representation of
said sensory activation, and optionally, using the representation
to determine optimal treatment.
[0094] In another aspect, there is provided a system for improving
specific human performance, the system including a processor, a
memory storing instructions that, when executed by the processor,
performs operations including identifying regions of the body
associated with parts of the brain that serve a specific function,
placing low energy stimulating electrodes proximate to said regions
of the body, applying stimulation through said electrodes to
activate said parts of the brain, and measuring changes in
performance related to said parts of the brain.
[0095] In another aspect, there is provided a method for
interacting with the human body, the method including detecting
bodily signals associated with one or more bodily functions at one
or more sensors associated with the human body, processing the
bodily signals to create one or more sensed signatures of the one
of more bodily functions, processing the signatures using an
enciphered functional network utilizing machine learning to
determine one or more effector responses needed to control a bodily
task, delivering via the enciphered functional network one or more
effector signals (the effector signals based on the one or more
effector responses), and controlling a bodily task.
[0096] In another aspect, there is provided a method to enhance
performance of a bodily task, the method including detecting
signals associated with the task at one or more sensors, processing
the signals to create one or more sensed signatures, processing the
signatures using an enciphered functional network to determine one
or more effector responses needed to enhance performance of the
bodily task, delivering via the enciphered functional network one
or more effector signals (the effector signals based on the one or
more effector responses), and enhancing performance of the
task.
[0097] In another aspect, there is provided a method for treating a
disease, the method including detecting signals associated with one
or more bodily functions at one or more sensors associated with the
human body, processing the signals to create one or more sensed
signatures of the one of more bodily functions, processing the
signatures using an enciphered functional network utilizing machine
learning to determine one or more effector responses needed to
treat a disease, delivering via the enciphered functional network
one or more effector signals (the effector signals based on the one
or more effector responses), and treating the disease.
[0098] In another aspect, there is provided a method for
transforming nerve activity associated with one or more bodily
functions, the method including detecting bodily signals of nerve
activity associated with the one or more bodily functions at one or
more sensors, processing the bodily signals to create one or more
sensed signatures of the one or more bodily functions, processing
the signatures using an enciphered functional network utilizing
machine learning to determine one or more effector responses needed
to transform nerve activity, delivering via the enciphered
functional network one or more effector signals (the effector
signals based on the one or more effector responses), and
transforming nerve activity.
[0099] In another aspect, there is provided a method for
controlling a device using an enciphered functional network, the
method including detecting bodily signals from a body using one or
more sensors, processing the bodily signals to create a sensed
signature, processing the sensed signature using an enciphered
functional network utilizing machine learning to determine one or
more effector responses to control the device, delivering via the
enciphered functional network one or more effector signals (the
effector signals based on the one or more effector responses), and
controlling the device.
[0100] In another aspect, there is provided a method to measure
bodily function in an animal, the method including detecting bodily
signals associated with sensory activation, processing the bodily
signals to create one or more sensed signatures, and processing the
sensed signatures using an enciphered functional network to
determine one or more effector responses needed to enhance the
bodily function of the animal.
[0101] In another aspect, there is provided a method of improving a
specific human performance, the method including identifying one or
more regions of a human body associated with parts of the brain
that serve a specific function, placing low energy stimulating
electrodes proximate to the one or more regions of the human body,
applying stimulation through the electrodes to activate the parts
of the brain, and measuring changes related to the parts of the
brain to verify improvement of the specific human performance.
[0102] In another aspect, there is provided a method for treating a
sleep disorder, the method including selecting one or more regions
of a patient's central nervous system and/or peripheral nervous
system associated with sleep functioning, and applying low energy
stimulation through electrodes to activate the patient's one or
more regions of central nervous system and/or peripheral nervous
system to treat the sleep disorder.
[0103] In another aspect, there is provided a method of enhancing
attention, the method including selecting one or more regions of a
patient's central nervous system and/or peripheral nervous system
associated with an attention disorder, and applying low energy
stimulation through electrodes to activate parts of a patient's
central nervous system and/or peripheral nervous system to treat
the attention disorder.
[0104] In another aspect, there is provided a method of treating
central sleep apnea, the method including identifying a target
region from one or more local areas of the head and neck (the
target region being functionally associated with one or parts of
the brain that control sleep), and delivering a therapeutically
effective amount of energy to stimulate the target region to treat
the central sleep apnea, while minimizing stimulation of other
regions of the body.
[0105] In another aspect, there is provided a method of modulating
mental function, the method including identifying a target region
selected from localized areas of the body (the target region being
functionally associated with parts of the brain that govern the
mental function), the mental function including one or more of
alertness, cognition, memory, mood, attention and awareness, and
delivering a therapeutically effective amount of energy to
stimulate the target region to modulate the mental function, while
minimizing stimulation of other regions of the body.
[0106] In another aspect, there is provided a system for
interacting with the human body, the system including a processor
and a memory storing instructions that, when executed by the
processor, performs operations including detecting bodily signals
associated with one or more bodily functions at one or more sensors
associated with the human body, processing the bodily signals to
create one or more sensed signatures of the one of more bodily
functions, processing the signatures using an enciphered functional
network utilizing machine learning to determine one or more
effector responses needed to control a bodily task, delivering via
the enciphered functional network one or more effector signals (the
effector signals based on the one or more effector responses), and
controlling a bodily task.
[0107] In another aspect, there is provided a system to enhance
performance of one or more tasks, the system including a processor
and a memory storing instructions that, when executed by the
processor, performs operations including detecting signals
associated with the task at one or more sensors, processing the
signals to create one or more sensed signatures, processing the
signatures using an enciphered functional network to determine one
or more effector responses needed to enhance performance of the
bodily task, delivering via the enciphered functional network one
or more effector signals (the effector signals based on the one or
more effector responses), and enhancing performance of the
task.
[0108] In another aspect, there is provided a system to treat a
disease, the system including a processor and a memory storing
instructions that, when executed by the processor, performs
operations including detecting bodily signals associated with one
or more bodily functions at one or more sensors associated with the
human body, processing the bodily signals to create one or more
sensed signatures of the one of more bodily functions, processing
the signatures using an enciphered functional network utilizing
machine learning to determine one or more effector responses needed
to treat a disease, delivering via the enciphered functional
network one or more effector signals (the effector signals based on
the one or more effector responses), and treating the disease.
[0109] In another aspect, there is provided a system to transform
nerve activity associated with one or more biological functions,
the system including a processor and a memory storing instructions
that, when executed by the processor, performs operations including
detecting bodily signals of nerve activity associated with the one
or more bodily functions at one or more sensors, processing the
bodily signals to create one or more sensed signatures of the one
or more bodily functions, processing the signatures using an
enciphered functional network utilizing machine learning to
transform nerve activity, delivering via the enciphered functional
network one or more effector signals (the effector signals based on
the one or more effector responses), and transforming nerve
activity.
[0110] In another aspect, there is provided a system to control a
device using biological signals, the system including a processor
and a memory storing instructions that, when executed by the
processor, performs operations including detecting bodily signals
from a body using one or more sensors, processing the bodily
signals to create a sensed signature, processing the sensed
signature using an enciphered functional network utilizing machine
learning to determine one or more effector responses to control the
device, delivering via the enciphered functional network one or
more effector signals (the effector signals based on the one or
more effector responses), and controlling the device.
[0111] In another aspect, there is provided a system to measure
visual function in an animal, the system including a processor and
a memory storing instructions that, when executed by the processor,
performs operations including detecting bodily signals associated
with sensory activation, processing the bodily signals to create
one or more sensed signatures representing quantitative measures of
sensation, and processing the sensed signatures using an enciphered
functional network utilizing machine learning to determine one or
more effector responses needed to enhance the bodily function of
the animal.
[0112] In another aspect, there is provided a system for improving
a specific human performance, the system including a processor and
a memory storing instructions that, when executed by the processor,
performs operations including identifying one or more regions of a
human body associated with parts of the brain that serve a specific
function, placing low energy stimulating electrodes proximate to
the one or more regions of the human body, applying stimulation
through the electrodes to activate the parts of the brain, and
measuring changes related to the parts of the brain to verify
improvement of the specific human performance.
[0113] In another aspect, there is provided a system for treating a
sleep disorder, the system including a processor and a memory
storing instructions that, when executed by the processor, performs
operations including selecting one or more regions of a patient's
central nervous system and/or peripheral nervous system associated
with sleep functioning, and applying low energy stimulation through
electrodes to activate the patient's one or more regions of central
nervous system and/or peripheral nervous system to treat the sleep
disorder.
[0114] In another aspect, there is provided a system to enhance
attention, the system including a processor and a memory storing
instructions that, when executed by the processor, performs
operations including selecting one or more regions of a patient's
central nervous system and/or peripheral nervous system associated
with an attention disorder, and applying low energy stimulation
through electrodes to activate parts of a patient's central nervous
system and/or peripheral nervous system to treat the attention
disorder.
[0115] In another aspect, there is provided a system to treat
central sleep apnea, the system including a processor and a memory
storing instructions that, when executed by the processor, performs
operations including identifying a target region from one or more
local areas of the head and neck (the target region being
functionally associated with one or more parts of the brain that
control sleep), and delivering a therapeutically effective amount
of energy to stimulate the target region to treat the central sleep
apnea, while minimizing stimulation of other regions of the
body.
[0116] In another aspect, there is provided a system to modulate
mental function, the system including a processor and a memory
storing instructions that, when executed by the processor, performs
operations including identifying a target region selected from
localized areas of the body (the target region being functionally
associated with parts of the brain that govern the mental function,
including one or more of alertness, cognition, memory, mood,
attention and awareness), and delivering a therapeutically
effective amount of energy to stimulate the target region to
modulate the mental function, while minimizing stimulation of other
regions of the body.
[0117] One motivation for this invention is that detailed
deterministic solutions for many complex bodily functions are
inherently limited for therapy. This reflects several factors.
First, there is inter-individual variation in regions of
control--for instance, the biological neural network to talk in one
person differs from the biological neural network to talk in
another. For functions with a nervous component, this may represent
the unique fashion in which higher cognitive functions and memories
are shaped during growth and development in each person or
potentially genetically established. Secondly, many brain functions
are plastic--changes in the environment or disease can alter
control regions. Changes can be gradual or abrupt, causing
variations over years, months or even weeks that may reflect normal
development, aging or dysfunction. This may explain why traditional
therapies that are initially effective become ineffective over
time. Thirdly, our conceptual knowledge of functional domains in
the central and peripheral nervous system is in its infancy. It is
thus a major challenge to understand a bodily function using a
classical paradigm of observing then stimulating a specific target
to map its region(s) of control.
[0118] Several innovations separate this invention from the prior
art. First, the invention creates an enciphered network for the
bodily function. This reconstructs the function as a network of
functional domains. This departs from the traditional approach of
meticulously mapping or recapitulating each biological organ
system. Instead, second, it identifies sensed signatures and
effectors for each function. Signatures can be nervous or
non-nervous system related. Third, the invention applies a feedback
loop to apply measured and quantitatively determined therapy that
may change over time even in the same individual. This is inherent
because the enciphered network can be trained over time using an
ongoing machine learning processes. Fourth, the core logic of the
invention is patient-tailored, distinct from the majority of
current devices that use "one-size-fits-all", generic or
stereotypic therapy. Fifth, therapy is adaptive through continued
machine learning, such that a similar abnormality in the same
individual may produce different signatures and/or require
different effector responses at two or more distinct periods in
time. Sixth, certain embodiments of the device combine biological
and non-biologic devices, together or individually. The enciphered
representation can accommodate novel signatures over time, that can
be extrinsic artificial signals as well as intrinsic biological
ones. Therapy can ultimately be delivered by an external device
and/or by direct stimulation or inhibition of an effector.
Embodiments include improvements of sleep apnea, the body's
response to heart failure, obesity, alertness, memory and mental
performance or cognition.
[0119] The concept of accessing functional domains for a task by
measuring from or stimulating an interconnected region of the
network, that may be neural, vascular or other, is novel at several
levels and has not been addressed by devices in the prior art. One
example way to better understand this concept is by considering the
disease of central sleep apnea.
[0120] The functional domain for central sleep apnea in this
invention includes sensed signatures of brain function (measurable
on the EEG), reduced oxygenation levels and increased carbon
monoxide levels in the blood (measurable from skin sensors), and in
some individuals increased heart rate and altered patterns of heart
rate and other less defined functions. Observed but unexplained
signatures may include nocturnal rostral fluid shift from the legs
(that may link sleep apnea with heart failure). Effector responses
for central sleep apnea is to nerve function to the neck muscles,
diaphragm, intercostal muscles and accessory muscles (measurable by
nerve firing rates). The present invention will use these sensed
signatures of brain or nerve activity, chest wall movement,
bioimpedance at the skin (to assess for a rostral fall), or
oxygenation for diagnosis and monitoring. In an embodiment for
treatment, the invention may result in varying effector
responses.
[0121] Chest wall impedance can be expressed in many forms. In this
invention, the sensed signature of abnormal chest wall impedance
includes a ratio of lower body impedance (e.g., leg, lower back) to
higher body impedance (neck and chest)--i.e., higher impedance in
lower body (less extracellular water), lower impedance in upper
body (more extracellular water). This could also be expressed as
upper-to-lower body conductance. This could also include measuring
impedance to different forms, patterns, or waveforms of electrical
energy.
[0122] In one preferred embodiment, machine learning is used to
associate repeatedly measured signatures with normal breathing. If
apnea arises during sleep, the invention will apply tailored
therapy adaptively to alter domain activity and alleviate sleep
apnea. The response to therapy (e.g., effector response) can also
be assessed repeatedly via sensed signatures, and the therapy can
be withdrawn or continued based upon these signatures. This differs
from the prior art in which therapies such as continuous positive
airway pressure or nerve stimulation are often delivered
empirically, continuously or in predetermined fashions without
adaptive algorithms to tailor therapy.
[0123] Other sensed signatures include altered nerve firing rates
for mental performance or sleep, vasodilation during sleep, reduced
skin galvanic resistance (from altered electrolytes or edema) in
the body's response to heart failure or sleep-breathing disorders,
altered skin absorption or emission of near-infrared or other
components of the electromagnetic spectrum during sleep disorders,
measured alterations to other forms of applied non-electrical
energy including optical signals (altered reflectance), sound or
ultrasound (different sonic reflectance and scattering), and
potentially altered spectroscopic signals of body chemistry that
can be sensed.
[0124] The network of functional domains is a unique approach for
interfacing with bodily functions. For instance, a patient with
heart pain (angina pectoris) or a heart attack (myocardial
infarction) often experiences "radiated pain" to the left arm,
shoulder or other regions. Some patients experience only arm pain
from cardiac ischemia--i.e., arm pain is a sensed signature. This
signature may not be relevant to other individuals a priori--but
can be learned by the enciphered network for that individual. In
this way, the invention can now detect nerve activity in the arm
below the typical nerve firing rates for sensed "pain", providing
the device with an early warning sensor for heart pain ("angina")
to provide therapy or alert medical personnel.
[0125] In another example, patients with problems of the abdominal
viscera (stomach, small intestine, large intestine) that may
include normal "indigestion" as well as diseases often experience
vague discomfort on the abdominal wall through imprecisely defined
and variable visceral and somatic nerves. Massaging this region is
an example of counter-stimulation that can alleviate the visceral
organ pain. In one embodiment, the invention will thus provide
algorithmically determined vibratory stimulation to appropriate
skin regions within the "functional domain" of the bodily function
to alleviate pain.
[0126] As yet another example, nerve firing in cutaneous or other
accessible nerves (e.g., mucous membranes of mouth, anus, or skin
of the external auditory meatus) may share neural control regions
with other organs, such as heart pain or even abnormal heart
rhythms. Effector signals can be delivered to specific regions of
the functional domain to alleviate heart pain or other
abnormalities. Other components of a functional domain may include
blood vessel flow, vasomotor reactivity, skin electrical
conductivity, heart rate or heart rate variations, breathing rate,
cellular edema and other indices illustrated throughout the
specification.
[0127] Therapy is individually tailored and not empirically
delivered. Baseline signatures such as rates and patterns of nerve
firing during a desired level of functioning are analyzed and
learned in each individual and may be combined with other
signatures within the enciphered functional network. In states such
as sleep-disordered breathing, heart failure, fatigue and others,
fluctuations outside this normal range are detected and can be used
to monitor disease or performance. Therapy such as stimulating neck
muscles for obstructive sleep apnea, stimulating accessory muscles
or alertness centers for central apnea, or therapy for heart
failure and other conditions can be monitored (e.g., by effector
response) and tailored to machine learned signatures. Functionality
can thus be modulated without direct knowledge or access to the
primary physiological target and without detailed
pathophysiological knowledge of that function.
[0128] Nerve signatures may be shared between many functions, e.g.,
based on dermatomal distributions of peripheral nerves. One example
is sensation of the tip of shoulder blade at the "C234" region,
control of deltoid muscle function by the "C56" region, and control
of the diaphragm muscles and hence breathing at the "C345" region.
Thus, sensation at the shoulder can indicate shoulder stimulation,
or pain in portions of the heart adjacent to the diaphragm.
Stimulation at these regions by direct electrical stimulation,
vibratory stimuli, heat or other can produce a counter-irritant to
the measured function.
[0129] Brain signatures can be assessed directly via the EEG or
simplified EEG measured from the scalp by many types of electrical
sensor. For instance, scalp activity in the alpha (7.5-12.5 Hz),
beta (12.5-30 Hz) or gamma (25-40 Hz) bands indicate states of
awakeness (wakefulness) or heightened or alertness; activity in the
delta (0.1-3 Hz) or theta (4-7 Hz) bands indicate drowsy (or
comatose) states. Depending on sensed activity, interventions can
be applied to the scalp or other domains of the network while
monitoring alpha, beta or gamma signatures to facilitate alertness.
In each case, the invention is novel in that it derives
patient-tailored signatures for a given function using machine
learning, and will apply interventions algorithmically in a
tailored feedback loop. In one preferred embodiment this will
enhance sleep function.
[0130] Peripheral nerve signatures are numerous. For instance,
increased nerve firing of the cervical sympathetic plexus in the
head and neck may be associated with alertness or rapid eye
movement (REM) sleep, and reduced activity may be associated with
drowsiness or stages I-IV of sleep. Stimulation of those regions of
the head and neck can be used to increase alertness. Increased
firing of the accessory (XI), facial (VII) or other cranial nerves
may indicate impending obstructive sleep apnea, and may provide
targets for therapy.
[0131] There are several non-nerve domain signatures. For instance,
deoxygenation of an oxygen sensor on the skin of a finger (via
optical reflectance or plethysmography) can indicate hypopnea or
apnea. Increased skin temperature or blood flow (absorption in red
wavelengths on an optical sensor) may occur in stages I-IV sleep
from parasympathetic activation. Novel skin sensors can detect
changes in biomarkers such as glucose (to detect diabetic states,
need to eat), INR (a test of blood thickness for some patients on
blood thinners) and a new generation of sensors for drugs in the
blood stream, chemical changes on the skin and so on,
Interpretation of these signatures can be troublesome but is linked
in this invention by machine learning to a specific function, e.g.,
fever increases skin temperature, but is accompanied by increased
breathing rate and altered skin biochemistry/impedance (due to
perspiration). By learning based on multiple signatures,
temperature information can be used in this case to distinguish
changes in breathing rates due to fever from that due to central
sleep apnea.
[0132] This invention adapts to concepts of neural plasticity.
Plasticity refers to alterations in the pathways of nerves and
connections (synapses) from changes in behavior, environment,
neural processes, thinking, and emotions, and also to changes
resulting from injury. This concept has replaced prior teachings
that the brain and nervous system are static organs. New studies
show that the brain changes in anatomy (structure) and physiology
(functioning) over time. There are several examples, e.g., DARPA
limb projects, stroke victims recovering function after months or
years of physical or occupational therapy despite having infarcted
the traditional brain areas for the target function. Plasticity is
also observed in peripheral nerves, for instance the distribution
of a functioning nerve (dermatome) can expand into an adjacent
distribution of diseased nerve supply. In other words, a singular
function can be assumed or subsumed by different regions of the
central or peripheral nervous system, that will also have
non-neural implications, e.g., on measured blood flow, galvanic
skin resistance or other physiological parameters.
[0133] This invention uses the principle that continuous machine
learning will enable its functionality to be retained even when
plasticity occurs, and again without precise physiological mapping
knowledge for that function. For instance, in classical Pavlovian
training, dogs were trained to salivate when exposed to
non-food-related stimuli that had previously been associated with
food in training. In other words, a new trained stimulus--function
interaction--was used without knowledge of detailed physiological
linking for that function.
[0134] This invention also encompasses personalized learned
feedback loops, to modulate a desired bodily function by
algorithmic machine learning analogous to classical Pavlovian
conditioning. In a training mode, stimulation is applied during
normal periods--for instance, vibratory stimulation of the skin of
the lower back on days of anticipated restful sleep. Subsequently,
if sleep is interrupted, trained modes of stimulation are applied.
This mode can be applied to various bodily functions including but
not limited to alertness, memory, sleep and sleep-disordered
breathing.
[0135] The present invention identifies functional domains
empirically, and provides computational customized, individualized
solutions. This differs from the prior art in which, for example,
preferred embodiment for sleep disordered breathing stimulate
cranial nerves (e.g., trigeminal or hypoglossal), but through
unclear mechanisms that may in fact inadvertently work by training
certain responses or stimulating other regions than those
intended.
[0136] In another set of preferred embodiments, the enciphered
network can be used to enhance body performance in non-disease
states. One direction is to utilize unused body capacity. In daily
western life, humans often underuse torso, leg and arm sensors and
effectors yet frequently use eye and hand sensors and effectors.
Stimulation of underused regions by a device can extend the sensory
capacity (bandwidth) of an individual. When combined with
artificial sensors, these underused regions can also be used to
provide a "sixth sense" (see drawings) to extend sensation to
biologically unsensed stimuli (e.g., a carbon monoxide sensor can
provide vibratory stimuli to unused portions of the body), to train
the body (e.g., improve alertness) or other function.
[0137] Enhancement of performance may require specific stimulation
patterns that vary based on frequencies, amplitudes and sites of
stimulation. This information can be derived by machine learning of
sensed signatures or patterns in each individual. Another approach
is to use patterns from individuals who are highly functioning in
that desired modality--from a de-identified database, by crowd
sourced data collection from wearable devices or by other means.
These patterns can then serve as inputs for machine learning
algorithms in the enciphered network, that will interface them to
the symbolic representation for an individual to tailor them
appropriately.
[0138] Effector stimulation should avoid inadvertent recruitment of
existing bodily functions by applying non-physiological or atypical
physiological stimuli. This can be achieved by using neural
frequencies or patterns that are not part of normal processing or
pathways, such as outside the normal sensed frequency, or with a
different pattern, or at a different (lower) amplitude. Using other
examples in this disclosure, the invention may detect subclinical
nerve firing in the functional domain for cardiac ischemia as an
early warning for angina, or application of subclinical amplitudes
of nerve stimulation to the accessory muscles to stimulate
breathing (for central sleep apnea) or neck (to improve alertness).
These safeguards will avoid invoking behavioral change, sensation
by the brain and/or changing memory of an event (Redondo et al.,
Nature 2014).
[0139] The invention can work with several types of sensors
individually or in combination. Examples include solid physical
sensors such as FINE
(http://singularityhub.com/2013/07/24/darpas-barin-controlled-prosthetic--
arm-and-a-bionic-hand-that-can-touch/), traditional ECG- or
EEG-electrical sensors, non-solid sensors such as electrostatic
creams, sensors for bioimpedance, piezoelectric film sensors,
printed circuit sensors, photosensitive film, thermosensitive film,
and external-oriented sensors not in contact with the body such as
video, IR, temperature, gas sensors, as well as other sensors.
These sensors detect stimuli and transduce the information through
a constructed/created (non standard or non-somatotopic) path to
active nerves.
[0140] Processing elements include a digital signal processor to
interface with output elements that can stimulate different
parts/nerves of the body, or cause mechanical action in an external
machine. Such elements could include traditional computing machines
with integrated circuits in isolation or networked (e.g., cloud
computing), biological computing, integrated biological/artificial
devices (cybernetic) or utilizing unused biological capacity to
perform specific, directed tasks. One potential embodiment is to
use unused computational capacity of the central nervous system to
perform pattern recognition in lieu of programming an artificial
computer for this purpose. This can be accomplished by training an
individual to recognize a visual/auditory/olfactory or other
sensation and then sensing the sensed signature of that evoked
response when that stimulus is subsequently encountered.
[0141] Effector elements can include direct electrical outputs,
piezoelectrical devices, visual/infrared or other stimulatory
systems, nerve stimulating electrodes or servo motors to control a
limb, digitized electronic signals such as radiofrequency or
infrared transmissions, or even virtualized data such as avatars in
a virtual world interface or elements in a large database that can
be queried, as well as other effector elements now existing or yet
to be developed.
[0142] Applications of effector elements can be for diagnostic
purposes such as detecting stimuli or body functions (e.g., visual
function, visual disease progression, mood, alertness, detecting
injury such as traumatic brain injury, cardiac electrical and/or
mechanical function, subclinical seizure detection), detecting
external world situations or environments without subjecting the
human body to discomfort (e.g., sensing heat in a fire, detecting
oxygen or toxic gas content in the external environment such as a
mine).
[0143] Effectors can be applied for medically related therapy such
as brain related function (e.g., brain stimulation for patients
with sleep disorders or central apnea, biofeedback for stroke
rehabilitation, deep brain stimulation for motion or seizure
disorders), other neurological diseases (e.g., substituting
artificial sensor data in patients with peripheral neuropathy,
biofeedback stimulation of muscles), cardiac disease (e.g.,
arrhythmias treated with implanted devices, cardiac function
improved with mechanical or electrical devices), response to
obesity, or other organ disease modified with directed electrical
or mechanical elements.
[0144] Applications of machine learned therapy using this invention
can be for training, learning and performing of physiological
activities or mechanical, non-physiologic functions. Unlike the
prior art that applies non-specific stimulation, e.g., transcranial
direct current stimulation (ref:
http://www.scientificamerican.com/article/amping-up-brain-function)-
, the present invention can sense, machine learn, optimize, and
then deliver specific therapy modulated via a feedback loop. This
will provide tailored therapy to modify many complex functions.
[0145] Other applications for this invention include improving
athletic performance after injury (e.g., direct stimulation to
muscles to regain lost function, biofeedback to maintain heart rate
within desired range during controlled exercise, brain
stimulation), enhancing sensory perceptions (e.g., artificial
visual sensors for facial recognition, artificial auditory sensors
to detect previously inaudible information), performing tasks in
non-typical ways by overcoming constraints or developing more
efficient solutions (e.g., driving a car with small finger
movements or eye motion amplified by artificial device, controlling
an external device biologically, e.g., small eye or limb movements
to control a computer interface). Examples of mechanical functions
include biological operation of a mechanical exoskeleton for
soldiers, performing tasks too difficult or dangerous for humans
such as deep sea exploration, armed combat, or basic tasks such as
controlling a computer, video games or remote controls.
[0146] In summary, the invention incorporates a combined
biological-artificial network, referred to as enciphered network
(or representation), to modulate specific tasks (such as complex
bodily functions often requiring brain or nerve involvement, or
higher cortical functions). Sensors (biological or artificial)
sense the activity of the measured task. This sensed activity is
enciphered, and then machine learning algorithms and specific
hardware modulate the network using biological, artificial or
hybrid effectors (e.g., stimulating electrodes). The network can
directly augment a function (e.g., sleep), or form a new function
via existing elements ("retasking" a function, e.g., associating
lower back stimulation with sleep).
[0147] The enciphered network can operate internally using symbolic
internal representation specific to each task. Specific
representations of each task may be important because the pattern,
frequency, and amplitude of stimulation differ considerably between
tasks--e.g., modulating electrical activity on the scalp versus the
neck or other parts of the body, or stimulating neural elements
versus blood vessels.
BRIEF DESCRIPTION OF THE DRAWINGS
[0148] Some embodiments are illustrated by way of example and not
limitation in the figures of the accompanying drawings, in
which:
[0149] Some embodiments are illustrated by way of example and not
limitation in the figures of the accompanying drawings, that
show:
[0150] FIG. 1. Schematic representation of the invention, including
sensors for biological and non-biological signals, a processing
unit that produces a symbolic representation of specific nervous
functions, or an "encyphered nervous system", and a control unit to
alter nervous function or control a device.
[0151] FIG. 2. Flowchart indicating general functionality of the
invention for biological and non-biological sensed signals.
[0152] FIG. 3. Flowchart indicating functionality of the invention
for biological sensed signals.
[0153] FIG. 4. Flowchart indicating functionality of the invention
for non-biological (external) sensed signals.
[0154] FIG. 5. Flowchart indicating one embodiment for enhancing
motor (muscle control) function of the nervous system. This is
illustrated for leg muscle function, for enhancement (e.g. in
military or sports use) or for medical purposes (e.g. after a
stroke).
[0155] FIG. 6. Flowchart for indicating one embodiment for
enhancing sensory perception/sensation of the nervous system. This
is illustrated for alertness, for enhancement (e.g. military or
sports use), for medical purposes (e.g. monitoring drowsiness or
coma) or for consumer safety (e.g. identifying drowsiness while
driving to control a feedback device).
[0156] FIG. 7. Flowchart indicating another embodiment for
transposing, or enhancing sensory perception. This is illustrated
for hearing, with the invention enhancing hearing and transposing
hearing function to another nervous function.
[0157] FIG. 8. Flowchart indicating another embodiment for
providing a sensory function that does not currently exist. This is
illustrated for integrating sensation from a biosensor for a
biotoxin.
[0158] FIG. 9. Flowchart indicating another embodiment for using
the invention and enciphered nervous system to provide de novo
functionality. Illustrated is using unused computational capacity
of the nervous system to perform pattern recognition in lieu of
programming an artificial computer for this purpose.
[0159] FIG. 10. Block diagram of an illustrative embodiment of a
general computer system.
[0160] FIG. 11 shows a schematic representation of the invention,
including biological sensors or external sensors, a signal
processing unit and a computing device that uses machine learning
and can interface a database to create a symbolic representation of
bodily functions, e.g., an "enciphered functional network". A
control unit can be used to treat abnormal physiological functions
via a device or biological organ ("effector") tailored by measuring
response to therapy in a feedback loop.
[0161] FIG. 12 illustrates the relative relationship of sensors,
sensed signatures for functional domain(s), the enciphered
functional network (with analysis engine), the effector group(s)
for a functional domain for a bodily function.
[0162] FIG. 13 shows a flowchart illustrating how the enciphered
functional network represents a bodily function in an individual
person, as functional domains represented by sensed signatures.
Sensed signatures are analyzed by machine learning algorithms
relative to desired and undesired behavior, and to databases in the
enciphered network of "population behavior" or historical behavior
of that individual, to monitor function, guide and assess response
to therapy.
[0163] FIG. 14 shows an example of sensed signatures for a given
bodily function, for functional domains representing physiology in
the nervous system and not in the nervous system. The portfolio of
sensed signatures becomes the measured representation of that
bodily function for an individual person.
[0164] FIG. 15 shows examples of modifying bodily function using
the enciphered network. Modification is tailored to the individual
via personalized sensory signatures and machine learning in the
enciphered network. Modification includes therapy, such as for
sleep-disordered breathing, but can also enhance normal function
for that individual. Modification operates in a continuous
feedback, assessing response via the enciphered network to prevent
excessive or deleterious modification.
[0165] FIG. 16 shows illustrative body locations for sensed
signatures and modifying various functional domains. Sensor
locations are indicated by open (white) regions and effector
(modifying) regions by filled (black) regions. Their relative size
varies in each individual, is determined by machine learning for
each individual and is not portrayed to scale.
[0166] FIG. 17 shows examples of a body sensor, with a sensor
element, power source, microprocessor element, nonvolatile storage
and communication element. Several types of sensor element are
illustrated, such as photodetector (for skin temperature, metabolic
light sensing, drug sensing), galvanometer (for skin impedance),
pressure (for weight, skin breakdown), temperature or chemical. The
invention can also use external sensors (FIGS. 11, 22-28) that
provide a variety of extrinsic or artificial signatures (FIGS.
22-28).
[0167] FIG. 18 shows an example of an embodiment of sensed
signatures in sleep disordered breathing.
[0168] FIG. 19 shows an example of an embodiment of effector
locations for sleep disordered breathing.
[0169] FIG. 20 shows an example of an embodiment of sensed
signature for heart failure.
[0170] FIG. 21 shows an example of an embodiment of sensed
signature of body response to obesity.
[0171] FIG. 22 shows an example of an embodiment of sensed
signatures for other conditions.
[0172] FIG. 23 shows an enciphered (symbolic) network model for
physiology of sleep-disordered breathing.
[0173] FIG. 24 shows enhancement of body function using enciphered
network.
[0174] FIG. 25 shows cybernetic enhancement of body function using
enciphered functional network.
[0175] FIG. 26 shows an example of a transformation of motor
function. The flowchart shows one embodiment for enhancing motor
(muscle control) function of the nervous system. This is
illustrated for leg muscle function, for enhancement (e.g., in
military or sports use) or for medical purposes (e.g., after a
stroke).
[0176] FIG. 27 shows an example of enhancing sensory function. The
flowchart indicates embodiment for enhancing sensory
perception/sensation of the nervous system. This is illustrated for
alertness, for enhancement (e.g., military or sports use), for
medical purposes (e.g., monitoring drowsiness or coma) or for
consumer safety (e.g., identifying drowsiness while driving to
control a feedback device).
[0177] FIG. 28 shows an example of transformation of sensory
function. The flowchart indicates an embodiment for transposing, or
enhancing sensory perception. This is illustrated for hearing, with
the invention enhancing hearing and transposing hearing function to
another nervous function.
[0178] FIG. 29 shows an example of creating a novel "cybernetic"
sensory function. The flowchart indicates an embodiment for
providing a sensory function that does not currently exist. This is
illustrated for integrating sensation from a biosensor for a
biotoxin.
[0179] FIG. 30 shows another example of creating a novel
"cybernetic" sensory function. The flowchart indicates an
embodiment for using the biological nervous system for recognition
of a desired pattern.
[0180] FIG. 31 shows computer hardware for machine learning.
DETAILED DESCRIPTION
[0181] FIG. 1 illustrates an example system to modify and enhance
functionality of the nervous system in a human being. Specifically,
the example system 100 is configured to access external signals
from biological sensors 104 and from external sensors 110.
[0182] Biological sensors 104 include, but are not limited to,
sensors for transcutaneous or invasive nerve activity (neural
electrical activity), muscle electrical activity (myopotentials),
of mechanical activity (mechanoreceptors), skin resistance (a
measure of body chemistry), body temperature (a measure of
metabolic activity and other disease states), body pH (from the
skin, mouth, or other regions of the gastro-intestinal or
genitourinary tracts), enzymatic profile (for instance, from a
probe in the gastrointestinal tract), DNA profile (for instance, a
gene chip on the lining of the mouth), heart rate, ventilating
(breathing) rate, or any other body signal.
[0183] External sensors 110 can sense biological signals, from that
individual, from another individual or from a database of signals
118.
[0184] External sensors 110 can provide many types of information
including, but not limited to, those normally sensed including
pressure/physical movement (tactile, touch sensation), temperature
(thermal sensation), sound (auditory sensation), electromagnetic
radiation in the visible spectrum (visual sensation), movement (a
measure of muscle function and balance).
[0185] External sensors 110 can provide information related to
normal sensation but that is not normally sensed including, but not
limited to, the invisible electromagnetic spectrum (such as gamma
radiation, X-rays, radiowaves), sound waves outside the normal
physiological range for humans (roughly 20 Hz to 20 kHz) but
including the range sensed by animals (for instance, dogs can sense
higher frequencies).
[0186] External sensors 110 can provide information that is not
normally sensed including, but not limited to, toxins such as
carbon monoxide or excessive carbon dioxide, forms of radiation
(such as alpha and beta radiation), biotoxins such as toxins of
Escherichia coli bacteria associated with food poisoning (type
0157), anthrax or other agents. Clearly, such information would be
of value for military and security applications.
[0187] In FIG. 1, signals (internal or external) are delivered
either wirelessly or via wired communication to a signal processing
device 114 functioning in concert with a computing device 116 that
has access to an analysis database 118. The computing and signal
processing devices communicate with a control device 120, that in
turn directly controls a biological device (e.g., a body part of
body function) 108 or an external device 112 (e.g., remote control,
medical device). The computing, signal processing and control
devices with sensors and effector devices together form an
"enciphered nervous system" (ENS).
[0188] FIG. 2 give more detail on the inventive process of the
"enciphered nervous system". Signals are sensed (205) as either
non-biological (210) or biological (215), although these signals
can be combined and multiplexed.
[0189] In FIG. 2, element 220 is the core computational element
that functions as the central nervous system of the ENS. This
computational element forms a symbolic relationship between the
signal and a biological function. This symbolic relationship is
mathematical. Notably, this relationship is empirical and
functional--it is not based on a detailed neurophysiological
mapping of the function. It is also not necessarily based on the
primary "classical" neurological loop. For instance, sufficient
pain in the leg causes elevated nerve activity in other parts of
the body. This can be sensed as "associated with the pain"
sensation, from a sensor in a more convenient part of the body. The
empirical functional relationship is mathematical, and can be
deterministic (e.g. equation based), or can be trained/learned such
as via neuronal network.
[0190] In the simplest case, the symbolic relationship is a matrix
in which a signal X causes a function Y; for instance, a noxious
stimulus such as pain sensed by a sensor/sensory nerve in the leg
(X) causes activity in a motor nerve causing withdrawal of that leg
(Y). This function is not represented in the device based upon a
detailed neurophysiological representation of leg sensation (in the
primary somatosensory cortex, in the post-central gyrus), or the
precise nerves that control the leg. Instead, this function is
mapped empirically--sensation on any nerve associated with the
painful stimulus can result in actions leading to leg
withdrawal.
[0191] An inventive advantage of this approach is that it exploits
the pleiotropic effects of any particular stimulus. For instance,
an acute painful stimulus often produces activation on nerves
remote from the original site of stimulation. Hence, pain in the
leg, that may be inaccessible, may be detected from nerve activity
quite distant from the sensation such as the chest wall, that may
be more accessible.
[0192] In FIG. 2, Step 220 is followed by a mechanical action (225)
or biological action (230). For instance, the sensed noxious
stimulus can produce an effector function to move the leg
(biological, 230), or control a device to administer a pain killing
medication or therapy (device, 225). In other examples that will be
discussed below, the stimulus can move a prosthetic limb
(mechanical, 225) or alter biological function (230).
[0193] FIG. 2 further indicates that the precise action is
determined by the ENS by defining the interaction with the device
or bodily function (step 235). This is a programmed function,
depending upon the desired functionality of the invention. This
then produces a real output requiring energy application (step 240)
that results in interaction with the device (245) or a bodily
function (250).
[0194] Thus, FIG. 2 summarizes the invention as a parallel
artificial central and peripheral nervous system that pairs sensed
function to effector function in programmed ways. In the simplest
case, the sensed and effector functions are natural physiological
functions, such as sensing a painful stimulus from the leg and
moving the leg away. In more complex and practical ways, the
invention provides the ability to enhance normal function
(performance enhancement), enhance impaired function to treat a
disease or in cases where normal function cannot be manifest (e.g.
in warfare or other situations of constraint).
[0195] The invention as described in FIG. 1 and FIG. 2 may require
general information on how certain sensed signals cause damage, to
calibrate sensing and delivery of therapy functions. For instance,
exposure to carbon monoxide is dangerous, yet this toxin is often
undetected. Federal agencies in the U.S. such as OSHA put a highest
limit on long-term workplace exposure levels of 50 ppm, with a
"ceiling" of 100 ppm. Exposures of 800 ppm (0.08%) lead to
dizziness, nausea, and convulsions within 45 min, with the
individual becoming insensible within 2 hours. The present
invention detects this toxin early and causes biofeedback through
the enciphered nervous system, hence having extremely practical
implications in industrial environments. Other nomograms to
identify thresholds for "safe" versus and "actionable" exposure to
various stimuli are contemplated, including but not limited to
chemicals, biological toxins, radiation, electrical stimuli, visual
stimuli and auditory stimuli. The present invention provides
methods and systems to detect and provide feedback mechanisms and
to develop such practical applications for therapy or creating
safer environments.
[0196] The invention as described in FIG. 1 and FIG. 2 can also be
used to create totally novel human functionality, by using the
engineered artificial "encyphered nervous system" to pair sensed
biological or external signals to any programmed biological or
external device function. It thus forms an embodiment of a
cybernetic nervous system operating in parallel with the body's
natural nervous system. The extent to which this these nervous
systems are parallel or integrated will depend upon the extent to
which sensed signals are multiplexed and effector "control" signals
are combined. Examples are discussed below.
[0197] The invention as described in FIG. 1 and FIG. 2 thus
provides hitherto unavailable programmatic control of
plasticity--that is actually observed at some level on a regular
basis in normal life. In the realm of sensory physiology, training
can enable an individual to perceive a sensation that was
previously present but not registered/recognized. Examples include
musical training to detect tonality, or combat training to detect
subtle sounds or visual cues. In the realm of motor control,
physical training can enable an individual to use muscle groups
that were previously unused. In the realm of disease, normal
"healing functions" cause undiseased regions of the central nervous
system to take over functions now lost due to a stroke (cortical
plasticity), or unaffected peripheral nerves to take over functions
of a nerve lost due to trauma or neuropathy (expansion/plasticity
of peripheral dermatomes).
[0198] The invention also substantially extends normal
plasticity--by programming desired and directed regions of the body
to sense and effect functions normally reserved for other regions
of the body that are currently inaccessible (e.g. in military
combat) or unavailable (e.g. due to disease).
[0199] The invention also substantially advances normal plasticity
by integrating external sensors (e.g. for normally inaudible sound
frequencies or sensations) or devices (e.g. prosthetic limbs, other
electronic devices) into the ENS.
[0200] Thus, this invention can improve and enhance function of
traditional senses, if a device is used that integrates sensors
that sense outside the normal physiological range can be used to
enhance the range of normal physiological sensation. For instance,
sensing signals in the "inaudible to humans" part of the frequency
spectrum, transducing the signal to the audible range, and
transmitting it via bony conduction using a device could be used
for private communication, encryption, recreational or other
purposes. Medically, this invention could be used to compensate for
hearing loss. This same invention with sensors of vibration could
be used to compensate for loss of this sensation in certain
neurological diseases such as peripheral neuropathy, by
transmitting this sensation to an intact sensation in a different
part of the body.
[0201] Important safety issues must be raised at this stage. While
no untoward, dangerous or otherwise undesired functionality has
been observed with this invention, certain limits must be imposed.
First, stimulation intensity provided by the device can be
controlled such that painful or dangerous levels are not reached.
Second, sensory input can be controlled such that disturbing or
undesired levels are not reached. Third, any sensor or device
(effector) used desirably may have acceptable and tested safety
profiles.
[0202] FIG. 3. Provides a flowchart for the ENS to sense biological
signals (305) and complete an effector function based upon this
information. The first step is the symbolic model, as described
above (310). This involves mapping a sensed signal to a
function--but not in the classical, detailed fashion typical of
neurophysiology. This is a practical mapping step, for which
mapping of secondary regions associated with a function is
sufficient if that secondary region is readily accessible.
[0203] In FIG. 3, step 315 is to transform a function, controlled
by an existing motor nerve (sensed biological signal). In step 320,
the sensed signal is "re-routed" to control a prosthetic device or
another muscle group. For instance, in the case of an amputee, the
signature of motor nerve input to the leg may be detected from the
skin above the amputation site. The range of sensed nerve activity
on the skin may typically be 7-15 Hz (depending on the precise
nerve). Sensing these signals, and mapping them to specific
movement of a prosthetic limb may enable control of said limb. This
control may require subsequent training--for instance, behavioral
training in which the individual attempts to flex the amputated
limb, and detecting the skin signals as those that will flex the
prosthetic limb in that person. Similar personalized mapping is
used to train other motions of the prosthesis. Thus, this invention
represents one embodiment of a personalized "enciphered nervous
system".
[0204] In FIG. 3, step 325 is a distinct function--to improve motor
performance. It is well known that electrical stimulation of nerves
that control a muscle can stimulate that muscle. The frequency and
amplitude of this nerve activity lies within a range, but may be
specific for an individual. Thus, this ENS function is to sense
motor nerve activity controlling the quadriceps femoris muscle, for
instance. The frequency and amplitude of nerve activity in regions
of the skin associated with contraction and relaxation of that
muscle are stored for an individual (part of the symbolic
representation). An external device is then used to reiterate this
functionality in a programmatic way. This can be used to stimulate
the muscle during rest, to perform isometric exercises that will
improve muscle function. This may also increase metabolic rate and
cause weight loss.
[0205] In FIG. 3, step 330 is another distinct function--to retask
biological motor activity to control a device. For instance,
instead of actually moving a finger to control a remote control
unit for an electronic device, the user may attempt to move that
finger without expending sufficient energy to move the finger.
Sensors on the finger are tuned detect this motor stimulation (that
may be low amplitude), and the symbolic representation in the ENS
converts this to signals representing play, pause, rewind or other
functions and transmits them to control said consumer remote
control unit. Clearly, this function can be extended to training an
individual to move a portion of the face to represent the "play"
function, and having a sensor transduce this function, and
similarly for other surrogate regions of the body and retasked
functions.
[0206] In FIG. 3, step 335 is a transformation of sensed signals.
This is another functionality of the invention. Step 340 involves
enhancing the sensed signal. An example is performance improvement
(step 345), involving augmenting biological senses using sensors
that detect outside of normally sensed ranges. For instance,
sensing signals using a sensor of "inaudible to humans" part of the
frequency spectrum, transducing the signal to the audible range,
and transmitting it via vibration (bony conduction) to the hearing
regions of the brain (auditory cortex) using a device could be used
for private communication, encryption, recreational or other
purposes. Medically, this invention could be used to compensate for
hearing loss. This same invention with sensors of vibration could
be used to compensate for loss of this sensation in certain
neurological diseases such as peripheral neuropathy, by
transmitting this sensation to an intact sensation in a different
part of the body.
[0207] Another example of performance improvement (step 345) is to
increase alertness. Stimulation of the scalp in the temporal region
and other function-specific zones can increase brain activity in
these regions. The invention tailors such stimulation to the
symbolic representation of awakeness (i.e. alertness). As a
corollary, drowsiness can be detected via the ENS and used as part
of a feedback loop to trigger low intensity stimulation elsewhere
on the body where a cutaneous device can be placed. This has
several applications, including detecting and trying to prevent
drowsiness while driving, in the intensive care unit during
pre-comatose states or during drug-overdoses, and as a monitor for
excessive alcohol or medication ingestion.
[0208] Sensors can detect alertness versus drowsiness from large
groups of neurons such as using electroencephalography (EEG) that
produce a wide range of frequencies. EEG signals, for instance,
have a broad spectral content but exhibit specific oscillatory
frequencies. The alpha activity band (8-13 Hz) can be detected from
the occipital lobe (or, in this invention from electrodes placed
over the occipital region of the scalp) during relaxed wakefulness
and increase when the eyes close. The delta band is 1-4 Hz, theta
from 4-8 Hz, beta from 13-30 Hz and gamma from 30-70 Hz. Faster EEG
frequencies are linked to thought (cognitive processing) and
alertness, and EEG signals slow during sleep and during drowsiness
states such as coma and intoxication.
[0209] In FIG. 3, step 350 uses the invention for de novo sensory
function. One example is creating a digital or cybernetic "sixth
sense"--that is, adding to the existing 5 senses using external
sensors to detect an extended set of stimuli. The set of sensors is
nearly infinite, but includes several of particular relevance to
the field of industrial or military use, including sensors for
alpha or beta-radiation. Once sensed, the ENS transduces this
signal to an existing sense, such as vibration delivered through a
skin patch to a relatively unused skin region e.g. lower back. A
combat soldier exposed to alpha or beta particles will now "feel"
radiation as a programmable/trainable set of vibrations in his
lower back. Similarly, sensors for carbon monoxide or other
respiratory hazards could be transduced as "sixth senses" into--for
instance--low frequency vibration on the nostril. This approach is
far more efficient than a visual readout or other existing
devices--because they use the ENS to essentially reprogram the
natural nervous system for these functions.
[0210] FIG. 4 illustrates a flowchart of an embodiment in which
non-biological signals are sensed (step 405) and processed by the
"encyphered nervous system". The symbolic representation between
the body function and sensed signals is now extended to a
non-personalized ENS (step 410), in order to incorporate external
signals of a generic form albeit potentially tailored to the
individual person. This ENS can be derived from a database of
multiple individuals, or by a technique such as crowd-sourcing in
which information from multiple persons connected by a social
network is used to provide functionality.
[0211] Step 415 in FIG. 4 involves designing a programmable body
function to be associated with the sensed external (non-biological)
signal. This function can include motor control of a device such as
prosthetic limb in step 420. Another example would be more far
reaching--to use an external trigger signal to improve function in
an existing natural muscle group (item 430). As described, skeletal
muscle is typically stimulated by nerve activity at a frequency of
7-15 Hz (that varies with the precise nerve distribution, see
Dorfman et al. Electroencephalography and Clinical Neurophysiology,
1989; 73: 215-224). Providing this stimulation can improve muscle
strength by stimulating it, and would enable performance
improvement of e.g. leg muscles from a programmable signal. A
medical example would be to treat central sleep apnea, by having an
external sensor of oxygen desaturation to activate a device that
stimulates the phrenic nerve and hence the diaphragm. This has
substantial clinical implications.
[0212] In FIG. 4 step 425, the invention uses an external signal to
improve performance in a sensory function. In an example already
used, hearing can be enhanced by using external sensors of auditory
signals outside the normal frequency range to be transduced to the
normal frequency range as vibrations delivered via bone conduction
to the cochlear nerve in the inner ear using a device placed near
the mastoid processes (e.g. attached to the side-arms of
eyeglasses).
[0213] In FIG. 4 step 440 the invention exploits the full potential
of the enciphered nervous system to create novel programmable
functionality by pairing an external sensed signal with intrinsic
nervous function. The example of performance improvement (sensory
or motor) in step 445 has been discussed.
[0214] In step 450, FIG. 4, the invention can provide de novo
functionality. A large proportion of cerebral processing power is
dormant at any given time, but may be activated subconsciously
during daily activity (e.g. daydreaming). The ENS can
programmatically access this capacity to use the intrinsic nervous
system as a computer. One task for which the human brain/nervous
system is particularly adept is pattern recognition. Recognition of
faces, spatial patterns and other complex datasets is performed by
people far better than by most artificial computers. The selected
example trains the individual to detect said pattern via repeated
exposure to an image. The biological response to this image
(symbolic representation) is detected by sensors on the temporal or
frontal scalp. Again, this is empirical mapping--and it is
sufficient to represent a secondarily activated region of the
brain/scalp. Once this is accomplished, then detection of the
pattern or a similar pattern will subconsciously trigger said
response, that can be sensed and coded as a "1" or "0" to control a
device (e.g. a pattern classifier computer) or cause a certain
function--such as to trigger an alarm if this is a dangerous
pattern/image.
[0215] The Flowchart in FIG. 5 provides a preferred embodiment to
transform leg movement. A symbolic model of motor nerve function,
sensed near the primary motor region (scalp, near the superior
portion of the contralateral precentral gyrus) or at a secondary
region, is associated with a plurality of leg motions in step 510.
Once done, this functional mapping can be reprogrammed using
external sensed signals (step 515) or signals not normally
associated with leg function (e.g. moving an index finger in
patients with leg disease or soldiers who cannot move their leg in
a certain task), or the existing signal (step 520). In step 525 a
signal multiplexor is able to mathematically associate the
non-associated or associated signals in order to control the
desired programmed function. In step 530 this is enhancement of the
biological leg function (e.g. via cutaneous/direct electrical
stimulation as described). In step 535 this is via control of a
prosthetic limb.
[0216] FIG. 6 provides an embodiment for enhancing sensory
alertness. The steps are analogous to the prior examples. The
symbolic model of scalp sensed nerve activity e.g. in the temporal
region is empirically associated with varying alertness levels
(self-reported or monitored) in step 610. This functional mapping
is reprogrammed using external sensed signals (step 615) or signals
not normally associated with alertness (e.g. a specific auditory
sensed frequency), or the existing scalp signal (step 620). In step
625 a signal multiplexor mathematically associates the
non-associated or associated signals to program the desired
function--electrical stimulation of the scalp to increase alertness
(step 630). Step 635 provides an alertness monitor that can provide
an alarm or actually result in stimulated function (to close the
artificial/cybernetic feedback loop in the enciphered nervous
system) to detect and try to avoid drowsiness, coma or toxin
ingestion.
[0217] FIG. 7 is a flowchart of an embodiment to enhance
performance in a sensory function--in this case hearing. Step 710
forms the symbolic representation using sensed signals from a
readily accessible sensor (not just ear, but potentially
secondarily associated skin regions). Step 715 uses sensors that
detect outside of normally sensed ranges in "inaudible to humans"
parts of the frequency spectrum. Step 720 uses a signal normally
associated with hearing. Step 725 uses a multiplexor and control
logic to transduce the signal to the audible range (step 730),
transmitted via vibration (bony conduction) to the hearing regions
of the brain (cochlear nerve/auditory cortex) using a device could
be used for private communication, encryption, recreational or
other purposes. Medically, this invention could be used to
compensate for hearing loss. This same invention with sensors of
vibration could be used to compensate for loss of this sensation in
certain neurological diseases such as peripheral neuropathy, by
transmitting this sensation to an intact sensation in a different
part of the body. Step 735 transduces this signal to a different
`surrogate` sensation.
[0218] FIG. 8 depicts an embodiment to use the ENS to integrate
functionality that does not exist in nature into a personalized
biofeedback loop--in this case, detecting a toxin. Examples include
inhalation of Carbon monoxide, a toxic gas that is colorless,
odorless, tasteless, and initially non-irritating, that is very
difficult for people to detect. Another example is exposure to a
biotoxin, that may not be sensed until symptoms and signs of a
disease occur hours, days or weeks later. The inventive approach to
provide a "sixth sense" (step 805) is cybernetic, since the toxin
may produce both a direct signal from a specific sensor (detected
at step 810) and an associated biological signal (step 815), that
are blended (or multiplexed) in the invention. Examples of a direct
signal from a dedicated sensor (element 810) are the chemical
detection of carbon monoxide, or a biological assay for an
infective agent (viruses, bacteria, fungi). Ideally, this sensor
operates in near-real time, although this is not a requirement and
if not the case will simply provide a slower, non-real time signal.
Examples of an associated biological signal to carbon monoxide--a
toxin that is traditionally considered `unsensed`--is the specific
cherry red colorimetric change of hemoglobin from carbon monoxide
and the non-specific reduction in oxygenated hemoglobin that
results when carbon monoxide binds to oxygen binding sites.
[0219] FIG. 8 further depicts that the enciphered nervous system of
the invention forms an associative symbolic representation (step
820) between the direct and associated biological sensed signals.
The symbolic relationship may include a direct mathematical
transform, such as a quantitative relationship of the sensed signal
to carbon monoxide or the associated biological signal of cherry
red discoloration of hemoglobin to biologically relevant
concentrations. The symbolic relationship may also use an
artificial neural network or other pattern-learning or relational
approaches to link e.g. elevated heart rate or oxygen desaturation
to the toxin.
[0220] In FIG. 8 step 825, signals are multiplexed in a non-linear
analytical fashion, as defined in the symbolic representation for
any specific toxin. Computer logic is then used to control a
biological or artificial effector device. Several therapy or
monitor functions can be programmed to close a biofeedback loop.
For instance, the signal from the normally unsensed toxin can be
transduced into a specific signal on a naturally sensed `channel`
(step 830), e.g. low intensity vibration on skin on the nostril
(intuitively linked with inhalation), or stimulation of skin over a
scalp region normally associated with deoxygenation. This latter
biofeedback uses information from training related to the
individual person (contributing to the personalized enciphered
nervous system), or a database of symbolic representations from
many individuals associating related stimuli (here, de-oxygenation)
to biological signals. This is an example of a population-based, or
potentially crowd-sourced encyphered nervous system. Another
biofeedback option is therapeutic (step 835)--delivery of an
antidote, by sending control signals to a device. For carbon
monoxide exposure, therapy includes increasing oxygen
concentrations (using hyperbaric oxygen in extreme cases) and
administering methylene blue.
[0221] Nomograms of the detrimental impact of sensed signals are
used to calibrate sensing and delivery of therapy functions from
the enciphered nervous system. For carbon monoxide, exposures at
100 ppm (0.01%) or greater can be dangerous to human health.
Accordingly, in the United States, Federal agencies such as OSHA
put a highest limit on long-term workplace exposure levels of 50
ppm, but individuals should not be exposed to an upper limit
("ceiling") of 100 ppm. Exposures of 800 ppm (0.08%) lead to
dizziness, nausea, and convulsions within 45 min, with the
individual becoming insensible within 2 hours. Clearly, detecting
this toxin early would have extremely practical implications in
industrial environments, for instance. Other nomograms can be
developed to identify thresholds for "safe" versus and "actionable"
exposure to various stimuli including but not limited to chemicals,
biological toxins, radiation, electrical stimuli, visual stimuli
and auditory stimuli.
[0222] FIG. 9 provides a flowchart for an embodiment in which the
enciphered nervous system enables access to the processing power of
the natural nervous system to perform an arbitrary task, in this
case pattern recognition (step 905). This embodiment of the
invention is based upon 3 concepts. First, that the brain is more
efficient at some tasks than even the most powerful and
well-programmed artificial electronic computers. Pattern
recognition, e.g. of faces, is an excellent example that is easily
accomplished by most people yet that is suboptimal by computers
even with very sophisticated programming. Second, that the brain
output from a presented stimulation can be sensed. Third, that the
brain has unused capacity that can be accessed for this purpose.
This third item presents safety limits, and in the case of pattern
recognition, the invention must not be used for bioencoding images
or data that would be emotionally harmful or sensitive.
[0223] Steps 910 and 915 mathematically link the pattern (e.g. a
face) to the biological sensed response--for instance, activity of
nerves in the scalp over the parietal lobes of the brain, or over
the forehead indicating "recognition". This is used to create the
elements of enciphered nervous system for this task (step 920).
This will be personalized, but can also take inputs from a
multi-person (population, crowd-sourced) encyphered nervous system.
Once this link has been made, then presentation of the pattern will
result in a "sensed" biological pattern, that is used in step 925
to deliver a "1" (recognized) or "0" (not recognized) to control a
device (step 930) (e.g. external computer classifier) or stimulate
the individual via a surrogate sensation (step 935) (e.g. vibration
at the left upper arm if a recognized pattern is detected). Uses
for this invention include pure biocomputing (pattern recognition
of familiar or abstract shapes/codes), formally encoding and
enhancing memory of faces for a particular person, and security
such that only a hostile pattern/face elicits a specific surrogate
sensation or activates a device. One other advantage of this
approach over waiting for a cognitive recognition of the pattern is
that this can function as a "background process" and/or provide
faster pattern recognition.
[0224] FIG. 10 is a block diagram of an illustrative embodiment of
a general computer system 1400. The computer system 1400 can be the
signal processing device 114 and the computing device 116 of FIG.
1. The computer system 1400 can include a set of instructions that
can be executed to cause the computer system 1400 to perform any
one or more of the methods or computer based functions disclosed
herein. The computer system 1400, or any portion thereof, may
operate as a standalone device or may be connected, e.g., using a
network or other connection, to other computer systems or
peripheral devices. For example, the computer system 1400 may be
operatively connected to signal processing device 114 and analysis
database 118.
[0225] In operation as described in FIGS. 1-9, the modification or
enhancement of the nervous system of the body by creating and using
an enciphered nervous system (ENS) as described herein can be used
to enhance performance in normal individuals or restore or treat
lost function in patients.
[0226] The computer system 1400 may also be implemented as or
incorporated into various devices, such as a personal computer
(PC), a tablet PC, a personal digital assistant (PDA), a mobile
device, a palmtop computer, a laptop computer, a desktop computer,
a communications device, a control system, a web appliance, or any
other machine capable of executing a set of instructions
(sequentially or otherwise) that specify actions to be taken by
that machine. Further, while a single computer system 1400 is
illustrated, the term "system" shall also be taken to include any
collection of systems or sub-systems that individually or jointly
execute a set, or multiple sets, of instructions to perform one or
more computer functions.
[0227] As illustrated in FIG. 10, the computer system 1400 may
include a processor 1402, e.g., a central processing unit (CPU), a
graphics-processing unit (GPU), or both. Moreover, the computer
system 1400 may include a main memory 1404 and a static memory 1406
that can communicate with each other via a bus 1426. As shown, the
computer system 1400 may further include a video display unit 1410,
such as a liquid crystal display (LCD), an organic light emitting
diode (OLED), a flat panel display, a solid state display, or a
cathode ray tube (CRT). Additionally, the computer system 1400 may
include an input device 1412, such as a keyboard, and a cursor
control device 1414, such as a mouse. The computer system 1400 can
also include a disk drive unit 1416, a signal generation device
1422, such as a speaker or remote control, and a network interface
device 1408.
[0228] The invention may include, as depicted in FIG. 10, the disk
drive unit 1416 may include a computer-readable medium 1418 in
which one or more sets of instructions 1420, e.g., software, can be
embedded. Further, the instructions 1420 may embody one or more of
the methods or logic as described herein. In a particular
embodiment, the instructions 1420 may reside completely, or at
least partially, within the main memory 1404, the static memory
1406, and/or within the processor 1402 during execution by the
computer system 1400. The main memory 1404 and the processor 1402
also may include computer-readable media.
[0229] The invention may also include, dedicated hardware
implementations, such as application specific integrated circuits,
programmable logic arrays and other hardware devices, can be
constructed to implement one or more of the methods described
herein. Applications that may include the apparatus and systems of
various embodiments can broadly include a variety of electronic and
computer systems. One or more embodiments described herein may
implement functions using two or more specific interconnected
hardware modules or devices with related control and data signals
that can be communicated between and through the modules, or as
portions of an application-specific integrated circuit.
Accordingly, the inventive system encompasses software, firmware,
and hardware implementations.
[0230] In accordance with the invention, the methods described
herein may be implemented by software programs tangibly embodied in
a processor-readable medium and may be executed by a processor.
Further, in an exemplary, non-limited embodiment, implementations
can include distributed processing, component/object distributed
processing, and parallel processing. Alternatively, virtual
computer system processing can be constructed to implement one or
more of the methods or functionality as described herein.
[0231] It is also contemplated that a computer-readable medium
includes instructions 1420 or receives and executes instructions
1420 responsive to a propagated signal, so that a device connected
to a network 1424 can communicate voice, video or data over the
network 1424. Further, the instructions 1420 may be transmitted or
received over the network 1424 via the network interface device
1408 (FIG. 10).
[0232] While the computer-readable medium is shown to be a single
medium, the term "computer-readable medium" includes a single
medium or multiple media, such as a centralized or distributed
database, and/or associated caches and servers that store one or
more sets of instructions. The term "computer-readable medium"
shall also include any medium that is capable of storing, encoding
or carrying a set of instructions for execution by a processor or
that cause a computer system to perform any one or more of the
methods or operations disclosed herein.
[0233] In a particular non-limiting, example embodiment, the
computer-readable medium can include a solid-state memory, such as
a memory card or other package, which houses one or more
non-volatile read-only memories. Further, the computer-readable
medium can be a random access memory or other volatile re-writable
memory. Additionally, the computer-readable medium can include a
magneto-optical or optical medium, such as a disk or tapes or other
storage device to capture carrier wave signals, such as a signal
communicated over a transmission medium. A digital file attachment
to an e-mail or other self-contained information archive or set of
archives may be considered a distribution medium that is equivalent
to a tangible storage medium. Accordingly, any one or more of a
computer-readable medium or a distribution medium and other
equivalents and successor media, in which data or instructions may
be stored, are included herein.
[0234] In accordance with the inventive embodiments, the methods
described herein may be implemented as one or more software
programs running on a computer processor. Dedicated hardware
implementations including, but not limited to, application specific
integrated circuits, programmable logic arrays, and other hardware
devices can likewise be constructed to implement the methods
described herein. Furthermore, alternative software implementations
including, but not limited to, distributed processing or
component/object distributed processing, parallel processing, or
virtual machine processing can also be constructed to implement the
methods described herein.
[0235] It should also be noted that software that implements the
disclosed methods may optionally be stored on a tangible storage
medium, such as: a magnetic medium, such as a disk or tape; a
magneto-optical or optical medium, such as a disk; or a solid state
medium, such as a memory card or other package that houses one or
more read-only (non-volatile) memories, random access memories, or
other re-writable (volatile) memories. The software may also
utilize a signal containing computer instructions. A digital file
attachment to e-mail or other self-contained information archive or
set of archives is considered a distribution medium equivalent to a
tangible storage medium. Accordingly, a tangible storage medium or
distribution medium as listed herein, and other equivalents and
successor media, in which the software implementations herein may
be stored, are included herein.
[0236] As stated above, a system and method for enhancing and
modifying complex functions of the nervous system and other parts
of the body are disclosed herein. In the following description, for
the purposes of explanation, numerous specific details are set
forth in order to provide a thorough understanding of example
embodiments. It will be evident, however, to one skilled in the
art, that an example embodiment may be practiced without all of the
disclosed specific details.
[0237] The invention modulates and enhances complex and higher
bodily functions by modulating a series of functional domains.
Typically, the complex function will include a component of brain
or nervous activity. One innovation is the creation of an
enciphered (symbolic) representation to model the complex function.
Such a representation model may also be called a network, and is
learned in this invention. This is created by, then used to
interpret sensed signals from functional domains that comprise the
function. The enciphered network is then used to effect change. In
one preferred embodiment, this is applied to improve sleep apnea,
but other embodiments modulate heart failure, obesity, alertness,
mood, memory and mental performance or cognition.
[0238] FIG. 11 illustrates an example system to modify and enhance
complex body functions in a human being. Specifically, the example
system 1000 is configured to access external signals from
biological sensors 1040 and from external sensors 1100.
[0239] The biological sensors 1040 can sense biological signals,
from an individual, from another individual, or from a database of
signals 1180. The biological sensors 1040 can be wearable.
[0240] External sensors 1100 can sense biological signals, from an
individual, from another individual or from a database of signals
1180. In turn, signals may arise from the central nervous system,
peripheral nervous system, cardiovascular system, pulmonary system,
gastrointestinal system, genitourinary system, skin or other
systems.
[0241] External sensors 1100 can provide many types of information
including, but not limited to, those normally sensed including
pressure/physical movement (tactile, touch sensation), temperature
(thermal information, infrared sensing), chemical (galvanic skin
resistance, impedance, detection of specific ions from the skin),
sound (auditory sensation), electromagnetic radiation in the
visible spectrum (visual sensation), movement (a measure of muscle
function and balance).
[0242] External sensors 1100 can also provide information related
to normal sensation but that is not normally sensed including, but
not limited to, the invisible electromagnetic spectrum (such as
gamma radiation, X-rays, radiowaves), sound waves outside the
normal physiological range for humans (roughly 20 Hz to 20 kHz) but
including the range sensed by animals (for instance, dogs can sense
higher frequencies).
[0243] External sensors 1100 can provide information outside normal
sensory modalities including, but not limited to, toxins such as
carbon monoxide or excessive carbon dioxide, forms of radiation
(such as alpha and beta radiation), biotoxins such as toxins of
Escherichia coli bacteria associated with food poisoning (type
0157), anthrax or other agents. Clearly, such information would be
of value for military and security applications.
[0244] In FIG. 11, signals are delivered either wirelessly or via
connected communication to a signal processing device 1140
functioning with a computing device 1160 that has access to an
analysis database 1180. The computing device 1160 and signal
processing device 1140 communicate with a control device 1200,
which in turn controls a biological device 1080 or an external
device 1120. The biological device 1080 is an effector device,
which can be wearable by the individual. The computing, signal
processing and control devices with sensors and effectors together
form an "enciphered functional network" (EFN).
[0245] FIG. 12 illustrates the relationship between sensors, sensed
signatures for functional domain(s), the enciphered functional
network (with analysis engine) and the effector group(s) within the
functional domain for a bodily function. At item 1500 one can see
the entire functional network domain for a particular bodily
function, such as sleep or breathing. At 1550 are illustrated
sensors 1, 2, . . . n that are used to provide sensed signatures
1600 for this functional domain. The enciphered functional network
1650 for this functional domain controls and analyzes the
information from the sensors and sensed signatures Of note, the
enciphered network can recruit additional sensors or stored
patterns (such as from a database, shown in FIG. 13) depending on
its learned or programmed behaviors. Many forms of analysis can be
performed as discussed below. Item 1700 shows that the enciphered
functional network includes communication with an effector group
for that bodily function, which in turn signals effectors 1, 2, . .
. n at step 1750. A key element of the invention is
interconnectivity and links between each element within/with the
enciphered functional network, indicated by double arrows.
[0246] FIG. 13 gives more detail on the enciphered functional
network for a normal bodily function or abnormal bodily functions.
The list of bodily functions addressed by this invention are broad,
and typically span multiple physiological systems (represented as
functional domains). They may include but are not limited to sleep,
sleep disordered breathing, cognition, mental performance, response
to obesity, response to heart failure.
[0247] In FIG. 13, a body function is represented by nervous system
2200 and non-nervous system (non-neural) 2600 networks. The
networks 2200, 2600 comprise respective functional domains 2300,
2700, defined by signatures 2400, 2800 based on a variety of
sensors. This produces nerve and non-nerve signatures for the body
function, which can be normal 2500 and abnormal 2900--or desired
2500 and undesired 2900. It should be noted that the networks can
interact via interactions 2250 and signatures may be inter-related
by relationships 2450.
[0248] Machine learning algorithms of the enciphered functional
network are enabled using artificial intelligence (autobot, fuzzy
logic circuits). This can be done via neural networks (e.g., 3
layer back-propagation networks or other designs), techniques of
deep learning, heuristics, linear classifiers or other forms of
fuzzy logic. An important feature of such systems is that they do
not need to know much about the specifics of human pathophysiology,
but need to learn information about factors influencing behavior
that is provided by the sensed signatures. They are thus well
suited to the problem of complex bodily functions that are often
incompletely defined or mapped pathophysiologically. This is not
structured by theoretical "textbook" classification schemes.
Certain elements of the system can be layered as rule-based, using
for instance deterministic solutions from a database such as the
dermatomal distribution of a nerve in the shoulder or the fact that
some fluctuations in skin oxygenation reflect heart rate.
[0249] Thus, the symbolic model of simple and complex functions is
akin to representing something that is visualized by an
"impressionist" painter rather than a detailed physiological
representation--by one trained in the "realist" school. Again, this
approach is based largely on the premise that in addition to the
primary physiological systems required for a task, that is
difficult to precisely define, secondary networked regions become
involved.
[0250] Machine learning nominally links signatures with normal
function 2500 in order to create a patient specific range to detect
abnormal function 2900 as outliers. In practice, the best results
are obtained when the machine learning algorithms perform repeated
pattern classification interactions 2550 between sensed signatures
for normal 2500 and abnormal 2900 functions. This interconnectivity
is necessary, but its complexity makes the system ideally suited
for a computational machine learning paradigm to modify and treat
the networks 2350.
[0251] In FIG. 13, digital learned representations enable
personalized diagnosis and therapy. A database of learned networks
(representations) between individuals forms the core of a
multimodal digital network of population health and disease, that
is actionable--i.e., can be used to monitor and treat disease or
improve performance. For health care or screening purposes, this
database (component 2150) is de-identified, but if individual
consent is obtained, e.g., in military or Institutional settings,
abnormalities can be traced from or applied to specific individuals
to improve their performance in the population. This forms the
basis for a novel approach to crowd-sourced health or wellness
screening, crowd-sourced disease monitoring, and crowd-sourced
delivery of therapy.
[0252] FIG. 14 provides detail of signatures sensed 3100 by the
invention to represent a given bodily function in an individual
person. Body functions comprise multiple functional domains,
broadly classed as primarily nervous system related and not nervous
system related physiology. Sensed nerve signatures 3150 would
typically represent the sensing location 3200, patterns of activity
3250 (e.g., periodic with a certain frequency spectrum, or more
complex and potentially represented non-linearly by fractal
dimension or measures of entropy), or rate of firing 3300 (e.g.,
the fundamental or "dominant" frequency of a spectrum or first peak
on an autocorrelation function).
[0253] Numerous other nerve-related parameters are possible, e.g.,
nuclear scans of neuro-tissue function, e.g., MIBG scanning for
autonomic ganglia, metabolic quantification using positron emission
tomography based sensor information, serum levels of norepinephrine
and other nerve-related signatures familiar to one skilled in the
art.
[0254] Non-nerve signatures 3350 represent other modalities 3400
that are not primarily in the nervous system. Represented
modalities have one or more defined signatures, e.g., hypervolemia
is detectable by reduced electrical impedance of tissue,
sympathetic activation via "clammy skin"-reduced galvanic skin
resistance and altered ionic composition, apnea via reduced
oxygenation measurable as reduced skin absorption in the
near-infrared end of the electromagnetic spectrum. These signatures
also possess information on location 3450, rate 3500 and temporal
patterns over time 3550. Numerous other parameters are currently
possible and may develop over time and be incorporated into this
invention, e.g., tissue concentrations of neurohormones such as
B-type natriuretic peptide or prolactin from a pharmacological
sensor, signal intensity from a photodetector to detect drug
concentrations in skin or cutaneous blood vessels, drug or alcohol
levels in exhaled breath from an oropharyngeal sensor, drug or
alcohol levels in urine from a penile sensor, and other sensors
relevant to the functional domain under consideration.
[0255] The network of sensed signatures exemplified in FIG. 14
becomes the measured representation of that bodily function for an
individual person. This is a form of "digital phenotype" of
components of the bodily function. It is recognized that nervous
and non-nervous physiological elements are deeply integrated
biologically, but this formulation is a convenient approach to
parameterize complex physiology into tracks that can be measured,
mathematically modeled and learned. Other more integrated
formulations are possible.
[0256] Note that not all possible measured signatures are needed
for the invention to work--in simple clinical practice, heart
failure can be monitored quite well from the simple measure of
weight gain alone; this invention uses machine learning to
mathematically weight the most important signature but also to use
information from whatever is currently available.
[0257] FIG. 15 illustrates modification of the bodily function
using the enciphered network, tailored to sensed signatures.
Modifications include therapy, such as for sleep-disordered
breathing, but can also include enhancement of normal function for
that individual. Modification through the enciphered network
operates via a feedback loop, in which response is measured to
prevent excessive or deleterious modification. Nerve-related
domains can be modified by direct energy delivery 4000 to stimulate
or suppress a domain. For instance, counterstimulation of skin on
the abdominal wall (e.g., by vibration via a piezoelectric device,
heat via an infrared generator) may suppress the sensation of pain
from organs supplied by visceral nerves of lumbosacral origin
(lower back). Domains 4100 may thus lie in the peripheral nervous
or in the central nervous system 4200, such as scalp stimulation to
modify cranial nerves or light delivery to modulate the ophthalmic
nerve or (indirectly) pineal gland activity. In this way, the
bodily function can be treated, enhanced or otherwise altered 4300.
Non-nerve domains can be modified 4400 in many ways including
vibratory stimulation via a piezoelectric device to stimulate a
muscle, infrared heat to reduce muscle spasm to modulate the domain
4500 and network 4600 to modify the bodily function 4300. Notably,
modification is individually tailored via personalized sensory
signatures and the enciphered network.
[0258] Modulation of nerve-related domains 4100 can be linked to
modulation of non-nervous domains by modulation connection 4150.
Moreover, the central and peripheral nervous network 4200 can be
linked to the non-nervous system physiologic network by network
connection 4250.
[0259] FIG. 16 indicates several potential body 5000 locations for
sensing signatures and modifying different functional domains.
Bodily functions can be measured by sensor 5050 and/or modified by
effector 5100 sites. Sensor locations are shown by open (white)
regions, and effector (modifying) locations by filled (black)
regions. Their relative size varies in each individual and is not
shown to scale. FIG. 16 indicates sensor locations on the body 5000
to detect signatures of the nervous 5350, cardiovascular 5400,
pulmonary 5400, gastrointestinal 5450, genitourinary 5500, skin
5500 and other domains. Body functions measured and/or modified by
the enciphered functional network include sleep and central sleep
apnea 5150, cognitive performance 5200 such as alertness,
obstructive sleep apnea 5250, and the bodily response to obesity
5300. A variety of signatures are indicated by way of example and
not to limit the scope of the invention. These are discussed in
more detail with regards to other figures in this disclosure.
[0260] FIG. 17 illustrates an example of a body sensor 6000,
comprising sensor element 6050, power source 6100, processing
components 6150, nonvolatile storage 6200 (e.g., E2PROM, powered
RAM), communication element 6250 on a structural platform 6300.
Several types of sensor elements are illustrated. Biological
sensors include, but are not limited to, photosensitive sensors
6400 to detect skin reflectance (indicating oxygenated hemoglobin,
and perfusion), galvanometers 6500 to detect skin impedance or
conductance (a measure of body chemistry), transcutaneous or
invasive nerve activity (neural electrical activity) or muscle
electrical activity (myopotentials), pressure detectors 6600 (to
detect pressure, e.g., weight, mechanical joint movement or
position), thermal detectors 6700 to detect temperature (a measure
of metabolic activity and other disease states), and chemical
detectors 6800 to perform assays for norepinephrine or drugs, body
pH from the skin, mouth, or elsewhere in the gastro-intestinal or
genitourinary tracts, enzymatic profile in the gastrointestinal
tract, DNA profile (for instance, a gene chip on the lining of the
mouth), and other sensors such as for heart rate, ventilation
(breathing).
[0261] The invention can also use external sensors (FIGS. 11,
22-28) that provide a variety of extrinsic or artificial signatures
(FIGS. 22-28) to provide cybernetic sensor inputs or effectors to
the enciphered functional network.
[0262] FIG. 18 indicates an example embodiment of sensed signatures
in sleep-breathing disorders. As typical for many bodily functions,
sleep-disordered breathing impacts multiple nervous and non-nervous
system domains. While all domains can be sensed, not all domains
need to be sensed in every patient, and the actual sensed domains
(and hence sensors) can be tailored to signatures in a given
individual and practical considerations. As seen in FIG. 18, sensor
types can include but are not limited to skin impedance, other
electrical sensors (nerve firing in the periphery and on the scalp,
and heart rate), temperature, chemical sensors, optical sensors of
skin color (that can detect oxygen saturation of peripheral blood),
motion sensors and pressure sensors.
[0263] FIG. 19 indicates example embodiments for various effector
or treatment options for sleep-disordered breathing using the
enciphered functional network. These are provided by way of example
and in no way limit the scope of effectors and treatment options
that can be provided for this condition or other bodily functions.
The body 8000 is interfaced with effector devices 8100, tailored to
each modality. For sleep apnea 8200 of the central type, examples
include direct stimulation of breathing centers including the brain
(via low energy scalp stimulation), accessory muscles in the neck
and the diaphragm. For obstructive sleep apnea, examples include
direct stimulation of pharyngeal and neck muscles to maintain tone
and prevent obstruction. For central sleep apnea, the invention can
activate pro-breathing centers, tricking the brain to breathe more
by stimulating sensors of low oxygenation/high carboxyhemoglobin in
the finger, by providing CO2 or equivalent index of low breathing
to regions of the periphery that are not harmful. For central and
obstructive forms of sleep apnea, there is evidence that chest
edema accumulates and can be measured as increased
rostral-to-peripheral ratio of skin impedance (FIG. 13).
Accordingly, controlled negative pressure in the lower extremities
8400 can reverse this rostral fluid accumulation. Direct
stimulation of pro-sleep centers by other methods 8500 include
stimulating the pineal gland through light exposure of the
appropriate wavelength in the visible and infrared spectra. Light
can be provided in patterns that are specific to each individual.
Other pro-sleep sensors include activation of vibratory sensors
8600 to mimic the somnorific impact of massage, or stimulation of
post-prandial satiety sensors 8700 including stimulating peripheral
skin sensors of hyperglycemia. Other specific stimuli can also be
provided as familiar to one skilled in the art of sleep disorders,
and can be added to the infrastructure of the invention as new
modalities and sensed signatures are developed.
[0264] FIG. 20 indicates an example embodiment of sensed signatures
in response to heart failure. As typical for many bodily functions,
heart failure impacts multiple nervous and non-nervous system
domains. While all domains can be sensed, not all domains need to
be sensed in every patient, and the actual sensed domains (and
hence sensors) can be tailored to signatures in a given individual
and practical considerations. As seen in FIG. 20, sensor types can
include but are not limited to skin impedance, other electrical
sensors (nerve firing in the periphery and on the scalp, and heart
rate), temperature, chemical sensors, optical sensors of skin color
(that can detect oxygen saturation of peripheral blood), motion
sensors and pressure sensors.
[0265] FIG. 21 indicates an example embodiment of sensed signatures
in response to obesity. As typical for many bodily functions,
obesity impacts multiple nervous and non-nervous system domains.
While all domains can be sensed, not all domains need to be sensed
in every patient, and the actual sensed domains (and hence sensors)
can be tailored to signatures in a given individual and practical
considerations. As seen in FIG. 21, sensor types can include but
are not limited to skin impedance, other electrical sensors (nerve
firing in the periphery and on the scalp, and heart rate),
temperature, chemical sensors, optical sensors of skin color (that
can detect oxygen saturation of peripheral blood), motion sensors
and pressure sensors.
[0266] FIG. 22 shows an example of sensed signatures for other
conditions. One example is for chronic obstructive pulmonary
disease which, as typical for many bodily functions, impacts
multiple nervous and non-nervous system domains. While all domains
can be sensed, not all domains need to be sensed in every
individual. The actual sensed domains (and hence sensors) can be
tailored to signatures in a given individual and practical
considerations. As seen in FIG. 22, sensor types can include but
are not limited to skin impedance, other electrical sensors (nerve
firing in the periphery and on the scalp, and heart rate),
temperature, chemical sensors, optical sensors of skin color (that
can detect oxygen saturation of peripheral blood), motion sensors
and pressure sensors.
[0267] FIG. 23 summarizes the invention, a computerized
representation of a complex body function, paired to biological and
artificial (cybernetic) sensors, and biological and artificial
(cybernetic) effectors. The enciphered functional network is
trained by machine learning algorithms for specific bodily
functions. In the simplest case, sensed and effector functions are
natural physiological functions, such as sensing a painful stimulus
from the leg and moving the leg away. In more complex embodiments,
the invention has the ability to enhance normal function
(performance enhancement), enhance impaired function (e.g.,
sleep-disordered breathing) or treat a disease or in cases where
normal function cannot be manifest (e.g., in warfare or other
situations of constraint).
[0268] More specifically, FIG. 23 outlines an enciphered network
for sleep-disordered breathing. The left panel shows the actual
physiology measured for sleep disordered breathing, while the right
panel shows the computerized representation of the enciphered
functional network.
[0269] In measuring the actual physiology of sleep-disordered
breathing in an individual 1200, biological signals are sensed
1205. These include biological signals of control regions 1210
including activation of the amygdala and other parts of the limbic
system that control alertness, wakefulness and relate to sleep.
These signals have scalp representations that can be detected by
skin nerve sensors, but can also be detected by medical devices
such as the BOLD signal from functional magnetic resonance imaging,
or metabolic images from positron emission tomography in medical
applications. Physiologically, sleep is also triggered from
intrinsic but natural signals such as darkness, sound (e.g.,
soothing music or the sound of waves), tactile sense (e.g., massage
of parts of the body). The intrinsic sleep control regions of the
brain 1210 then integrate these inputs with sensors related to
breathing including low oxygenation, measureable in the fingertips
1225, that stimulates breathing, and stimulation of the diaphragm
1220 to enable ventilation of the lungs.
[0270] The schematic shown in the left panel of FIG. 23 is of
course a simplified view of sleep-related-breathing, but it
illustrates how a series of sensors and effectors are integrated by
the biological control regions. Other sensors and effectors can be
involved at other times, and can be measured in connection with the
sleep-related breathing. This is a strength of the invention, that
additional sensed signals can be added and will be adaptively
integrated by the enciphered network.
[0271] In the right panel of FIG. 23, the parallel enciphered
network for sleep-disordered breathing also has sensors, control
logic and effectors, but these are a combination of biological and
engineered (artificial) components. Sensors can detect intrinsic
signals 1240 (such as oxygen saturation) or extrinsic signals 1245
(such as the presence, intensity and patterns of visible light). A
sensor matrix 1250 then combines these biological and
non-biological signals either separately or by multiplexing them,
e.g., using a weighted function. The computational logic 1255 is
the central processor of the enciphered functional network.
[0272] The computational element 1255 forms a symbolic relationship
between sensed signals and biological function (e.g., elements
1250-1275 in FIG. 23). It is linked to a database 1260 to store
multiple states for this individual person as training datasets for
machine learning (i.e., fuzzy logic, artificial intelligence) in
order to learn normal sleep patterns and breathing from disordered
ones (elements 250 versus 290 in FIG. 2). This is then mapped to
effectors 1265 that can be biological, such as brain regions
(related to control regions 1210 and unrelated to control regions
1210) as well as muscles (the diaphragm 1220 as well as other
muscles that are less notable but also involved in sleep such as
the levator labii superioris alaeque nasi muscles). Effectors can
also be cybernetic 1275, in that they interface artificially
engineered devices with the body. For instance, a peripheral low
oxygen state can be mimicked by small wearable chambers ("treatment
gloves") surrounding one or more fingers that will stimulate
breathing from intrinsic sleep-brain control centers (control
regions 1210). Similarly, appropriate learned patterns of light or
of vibratory stimuli can be applied using appropriate devices, to
stimulate sleep-breathing patterns learned from normal states and
stored on the database 1260.
[0273] The symbolic relationship of the enciphered network in FIG.
23 is a mathematical relationship. This relationship is empirical
and functional that uses machine learned relationships between
sensed signatures and body function in each individual--and not on
detailed neurophysiological mapping. It is thus distinct and may
not be concordant with "classical" neurophysiology. For instance,
sufficient pain in the leg causes elevated nerve activity in other
parts of the body. This will produce "associated with leg pain"
signals in sensors located more conveniently in the body. The
empirical functional relationship is mathematical, and can be
deterministic (e.g., equation based), or can be trained/learned
such as via neural network.
[0274] In the simplest case, the enciphered symbolic relationship
is a matrix in which a signal X causes a function Y; for instance,
a noxious stimulus such as pain sensed by a sensor/sensory nerve in
the leg (X) causes activity in a motor nerve causing withdrawal of
that leg (Y). This function is not represented in the device based
upon a detailed neurophysiological representation of leg sensation
(in the primary somatosensory cortex, in the post-central gyrus),
or the precise nerves that control the leg. Instead, this function
is mapped empirically--sensation on any nerve associated with the
painful stimulus can result in actions leading to leg
withdrawal.
[0275] The advantage of this approach is that it can analyze the
multiple effects of a particular stimulus. For instance, an acute
painful stimulus often produces activation on nerves remote from
the original site of stimulation. Hence, pain in the leg, that may
be inaccessible, may be detected from nerve activity quite distant
from the sensation such as the chest wall, that may be more
accessible.
[0276] In FIG. 23, generalizing from the example for
sleep-breathing, sensing is processed and results in output to an
effector. For instance, the sensed noxious stimulus can produce an
effector function to move the leg, or control a device to
administer a pain killing medication or therapy. In other examples
that will be discussed below, the stimulus can move a prosthetic
limb or alter biological function.
[0277] Moreover, FIG. 23 shows that the enciphered network
determines precise action by defining interactions with the device
or bodily function. This is a programmed function, depending upon
the desired functionality of the invention. This then produces a
real output requiring application of energy that results in
interaction with the device or a bodily function.
[0278] FIG. 24 illustrates a preferred mode of action of the
invention to provide computational enhancement of the bodily
function via the enciphered functional network. The flowchart for
the invention senses signatures for a given bodily function 1305,
comprising biological signals (e.g., breathing rate, finger
oxygenation) or extrinsic signals (e.g., tissue impedance
indicating volume load, emitted infrared indicating temperature, or
carbon dioxide concentrations in exhaled air indicating the
efficiency of breathing).
[0279] Item 1310 applies the symbolic model of the enciphered
network, as identified in FIG. 18 to map sensed signals to a bodily
function based on practical measurable signatures rather than
classical, detailed physiology mapping that may be ill-defined,
rapidly changing and inaccessible to measurement.
[0280] As described above, the symbolic model uses machine learning
to map sensor input to normal and abnormal function of that bodily
functionality. This comprises training sets of different patterns
for that individual, that are both personalized and continuously
adaptive.
[0281] In FIG. 24, step 1315 transforms an effector (motor)
function, i.e., controlled by an existing motor nerve. In step
1320, the motor nerve signal is "re-routed" to control a prosthetic
device or another muscle group. For instance, in the case of an
amputee, the signature of motor nerve output to the leg may be
detected from the skin above the amputation site. The range of
sensed nerve activity on the skin may typically be 7-15 Hz
(depending on the precise nerve). Sensing these signals, and
mapping them to specific movement of a prosthetic limb may enable
control of the limb. This control may require subsequent
training--for instance, behavioral training in which the individual
attempts to flex the amputated limb, and detecting the skin signals
as those that will flex the prosthetic limb in that person. Similar
personalized mapping is used to train other motions of the
prosthesis. In this instance, the invention is one embodiment of a
personalized "enciphered nervous system".
[0282] In FIG. 24, step 1310 is another embodiment--to enhance
performance of this body function. Instead of expending the energy
required to move a finger, the enciphered network can sense
sub-threshold activity of the motor nerve and "boost" the signal to
move the finger 1314. This is useful for individuals with nerve
degeneration, those with musculoskeletal disorders or those under
some form of sedation who would normally not be able to communicate
via this finger.
[0283] Furthermore, the invention can artificially generate signals
needed to stimulate the muscle (FIG. 24, 1312). Since the frequency
and amplitude of nerve activity that controls a muscle lies within
a range for each individual, the enciphered network can simulate
the nerve activity controlling the quadriceps femoris muscle and
deliver it programmatically to regions of the skin associated with
contraction and relaxation of that muscle for that individual (part
of the functional domain). This can be used when the nerve is
degenerated or anesthetized (for instance, to prevent pressure
ulcers in patients on prolonged ventilation). It can also be used
for performance enhancement--for instance, to perform isometric
exercises during rest or sleep to prevent or reverse muscle
atrophy, or to improve muscle function or increase metabolic rate
to lose weight.
[0284] In FIG. 24, step 1330 is another embodiment of the
invention- to retask biological motor activity. In this case, it is
directed to control an artificial device. This cybernetic
application is further developed in FIG. 24. In FIG. 25, instead of
actually moving a finger to control a remote control unit for an
electronic device, nerve activity below the threshold of actually
moving that finger will control the device. This enables
functionality without expending as much biological energy, and also
in individuals who have lost biological function or are constrained
and unable to perform that motor function (e.g., in a military
situation). Sensors on the finger detect this subthreshold motor
nerve activity (e.g., of lower amplitude than biologically required
to move the finger), and the enciphered network converts this to
signals that represent play, pause, rewind or other functions and
transmits them to control the remote control unit. This may be for
a consumer device. Clearly, this function can be extended to
training an individual to move a portion of the face to represent
the "play" function, and having a sensor transduce this function,
and similarly for other surrogate regions of the body and retasked
functions.
[0285] In FIG. 24, step 1335 is a distinct embodiment that
transforms sensed signals. Step 1340 retasks the sensed signal. For
instance, sensation of a specific smell that is trained over time,
can elicit a different response or control a device. Step 1345
improves performance, augmenting biological outside of normally
sensed ranges. For instance, sensing signals in the "inaudible to
humans" frequency range, transducing the signal to the audible
range, and transmitting it via vibration (bony conduction) to
auditory regions of the brain (auditory cortex) could be used for
private communication, encryption, recreation or other purposes.
Medically, this invention could be used to treat hearing loss. This
same invention with sensors of vibration could be used to
compensate for loss of this sensation in diseases such as
peripheral neuropathy, by transmitting this sensation to an intact
sensation in a nearby or remote part of the body.
[0286] Another embodiment of performance improvement (step 1345) is
to increase alertness. Stimulation of the scalp in the temporal
region and other function-specific zones can increase brain
activity in these regions. The invention tailors stimulation to the
enciphered representation of awakeness (i.e., alertness). As a
corollary, drowsiness can be detected by the enciphered network and
used in a feedback loop to trigger low intensity stimulation by a
cutaneous device elsewhere on the body. This has several
applications, including detecting and trying to prevent drowsiness
while driving, in the intensive care unit during pre-comatose
states or during drug-overdoses, as a monitor for excessive alcohol
or medication ingestion, or during excessive fatigue states, e.g.,
in the military.
[0287] Sensors can detect alertness versus drowsiness from large
groups of neurons using electroencephalography (EEG) over a wide
range of frequencies. EEG signals have a broad spectral content but
exhibit specific oscillatory frequencies. The alpha activity band
(8-13 Hz) can be detected from the occipital lobe (or from
electrodes placed over the occipital region of the scalp) during
relaxed wakefulness and increase when the eyes close. The delta
band is 1-4 Hz, theta from 4-8 Hz, beta from 13-30 Hz and gamma
from 30-70 Hz. Faster EEG frequencies are linked to thought
(cognitive processing) and alertness, and EEG signals slow during
sleep and during drowsiness states such as coma and
intoxication.
[0288] In FIG. 24, step 1335 is a function detecting and/or forming
a de novo function. One example is creating a cybernetic "sixth
sense"--that is, adding to the 5 biological senses using artificial
sensors to detect an extended set of stimuli. The set of sensors is
nearly infinite, but includes several of particular relevance to
the field of industrial or military use, including sensors for
alpha or beta-radiation. Once sensed, the enciphered network can
transduce this signal to an existing sense, such as vibration
delivered through a skin patch to a relatively unused skin region,
e.g., lower back. A combat soldier exposed to alpha or beta
particles will now "feel" radiation as a programmable/trainable set
of vibrations in his lower back. Similarly, sensors for carbon
monoxide or other respiratory hazards could be transduced as "sixth
senses" into--for instance--low frequency vibration on the nostril.
This approach is far more efficient than a visual readout or other
existing devices--because they use the enciphered network to
essentially reprogram the natural nervous system for these
functions.
[0289] FIG. 25 generalizes cybernetic enhancement of body function
using the enciphered network. This is a further application beyond
the use of intrinsic biological signals. One application is to
apply purposeful interventions when natural body functions are
constrained, e.g., a soldier can use a finger to activate a device
if his/her foot cannot activate a pedal due to an obstacle, or, in
an amputee, interfacing a robotic arm to specific nerve fibers that
formerly controlled the biological arm.
[0290] FIG. 25 is an embodiment in which intrinsic biological
signals and extrinsic non-biological signals are sensed (step
1360). The enciphered network does not simply map learned function
to sensed signals, but instead extrapolates from learned functions
to create novel function 1365. The enciphered representation of the
body function to sensed signals is extended to a personalized
network in step 1365 via machine learning. This involves a series
of steps, including 1370 multiplexing or otherwise combining
intrinsic with extrinsic signals, to programmatically modify
external signals in a personalized fashion. Signal multiplexing is
performed to achieve the desired function 1375 that may be storage
of non biological information (e.g., word processing documents,
images) in the patient's brain, i.e., using biological storage as
digital memory, and so on. Signals can be combined based on data
from this person alone, from a database of multiple individuals
(e.g., item 1260 in FIG. 23), or by a technique such as
crowd-sourcing in which information from multiple persons is
integrated to train the enciphered network. Data from multiple
persons could be combined in a formal database, or by applying
machine learning to the wider set of sensed signals and biological
outputs between individuals (not just for one individual).
[0291] Step 1380 in FIG. 25 shows the effector layer, the interface
between the output of the enciphered network for a designed
cybernetic function and a series of biological (e.g., motor nerve,
muscle) or external (e.g., prosthetic limb, computer) effector
devices.
[0292] Several embodiments exist. In step 1385, the invention uses
a biological signal to control an external device (e.g., motor
nerve control of a prosthetic limb), or an external signal to
control a biological function (e.g., external signal stimulation of
a skeletal muscle). As described, skeletal muscle is typically
stimulated by nerve activity at a frequency of 7-15 Hz (varying
with precise nerve distribution, see Dorfman et al.
Electroencephalography and Clinical Neurophysiology, 1989; 73:
215-224). Such external stimulation can improve muscle strength by
stimulating it, and would enable performance improvement of, e.g.,
programmable improvement in leg muscle function. Another example is
to treat central sleep apnea, using an external sensor of oxygen
desaturation ("desat") to activate a device that stimulates the
phrenic nerve and hence the diaphragm. This may have substantial
clinical implications.
[0293] FIG. 25 step 1390 shows an embodiment in which the invention
replaces a biologically lost or unavailable function in that
individual with function from the enciphered network. This is an
extension of boosting performance in FIG. 24 (step 1325). For
instance, the unavailable function of hearing outside the normal 20
Hz to 20 KHz range can be provided using external sensors and the
signal transduced to the audible frequency range (e.g., vibrations
delivered via bone conduction to the inner ear using a device
placed near the mastoid processes, e.g., attached to the side-arms
of eyeglasses) or to another sensible modality (e.g., vibration on
the arm). In an individual with hearing loss, the sensed signal
will lie within the normal but compromised auditory range for this
individual.
[0294] In FIG. 25 step 1392, the invention enables biological
control of a computer. An example of this function is to provide an
intelligent control framework for an infusion pump. For instance,
glucose control is not determined simply by the reaction of the
pancreas and other sensing regions to plasma glucose. Instead,
higher brain centers that control activities of daily living
anticipate actions such as imminent exercise or stress, and produce
increased heart rate and a hormonal surge (e.g., adrenaline,
epinephrine) that in turn increases blood glucose. Current glucose
infusion pumps actually cannot mimic such higher cognitive input,
and instead wait for drops in glucose from metabolic demands before
infusing glucose. Such devices will always lag behind ideal
physiological control and will produce suboptimal performance.
[0295] In FIG. 25 step 1393, the invention can provide de novo
functionality. This exploits the full potential of the enciphered
functional network, in this case for the nervous system, and
extends beyond sensory or motor performance improvement in steps
1325 (motor) or 1345 (sensory).
[0296] In FIG. 25 step 1393, novel functionality can be provided
for motor function (i.e., previously unavailable movements) or
sensory function (i.e., a cybernetic 6.sup.th sense). A large
proportion of cerebral processing power is dormant at any given
time, but may be activated subconsciously during daily activity
(e.g., daydreaming). The enciphered network can access some of this
brain capacity to use the biological nervous system as a computer.
One task for which the human brain/nervous system is particularly
adept is pattern recognition. Recognition of faces, spatial
patterns and other complex datasets is performed by people far
better than by artificial computers. The selected example trains
the individual to detect the pattern via repeated overt or
subclinical exposure to an image. The biological response to this
image (symbolic representation) is detected by sensors on the
temporal or frontal scalp. Again, this is empirical--the primary
memory encoding regions do not have to be identified or mapped, and
it is sufficient to sense a secondarily activated region of the
brain/scalp. Once this is accomplished, detection of the pattern or
a similar pattern will subconsciously trigger the response that can
be sensed and coded as a "1" or "0" to control a device (e.g., a
pattern classifier computer) or cause a certain function--such as
to trigger an alarm if this is a dangerous pattern/image.
[0297] FIG. 26 illustrates an embodiment of motor function
controlled by the enciphered network. The Flowchart in FIG. 26
provides a preferred embodiment to transform leg movement. A
symbolic model is to link motor nerve function, sensed at a
signature of the primary motor region (scalp, near the superior
portion of the contralateral precentral gyrus) or a secondary
region, with a plurality of leg motions in step 1510. Once done,
functional mapping can be reprogrammed using external sensed
signals (step 1515) including those not normally associated with
leg function. An example would be for motion in an index finger to
control the leg movement, in patients with leg disease or soldiers
who cannot move their leg in a certain task. Functional mapping can
also use the existing signal (step 1520).
[0298] In step 1525, a signal multiplexor links the intrinsic or
extrinsic signals in order to control the desired programmed
function. In step 1530, this is achieved to enhance biological leg
function (e.g., via cutaneous/direct electrical stimulation as
described). In step 1535, this is performed to control a prosthetic
limb.
[0299] FIG. 27 shows an embodiment of enhancing sensory function
via the enciphered network. FIG. 27 is an embodiment for enhancing
alertness. A symbolic model is created in step 1610 using a
signature of sensed scalp nerve activity, e.g., from the temporal
region that is empirically associated with alertness. Functional
mapping is reprogrammed using intrinsic sensed signatures (step
1615) or signals not normally associated with alertness (e.g., a
specific auditory sensed frequency), or the existing scalp signal
(step 1620). In step 1625, a multiplexor links the intrinsic and
extrinsic signals with an effector to achieve the desired
function--electrical stimulation of the scalp to increase alertness
(step 1630). Step 1635 provides an alertness monitor to alarm or
produce the desired function, and that can detect and try to avoid
drowsiness or coma, such as during driving, on the battlefield or
from toxin ingestion.
[0300] FIG. 28 depicts an embodiment of the invention to transform
sensory function. FIG. 28 is a flowchart of an embodiment to
enhance sensory performance--in this case hearing. Step 1710 is the
symbolic representation of sensed signals from a readily accessible
sensor of the signature near the ear, as well as secondarily
associated skin regions. Step 1715 uses sensors to detect
signatures of frequencies outside the normally sensed frequency
spectrum. Step 1720 uses a signal normally associated with hearing.
Step 1725 uses a multiplexor and control logic to transduce the
signal to the audible range (step 1730), transmitted via vibration
(bony conduction) to the hearing regions of the brain (cochlear
nerve/auditory cortex) using a device that could be used for
private communication, encryption, recreational or other purposes.
Medically, this invention has application as a sophisticated
hearing aid. This same invention with vibration sensors compensates
for loss of this sensation in diseases such as peripheral
neuropathy, by transmitting this sensation to an intact sensation
in a different part of the body. At 1735, the multiplexor
transduces this signal to a different "surrogate" sensation, e.g.,
skin stimulation.
[0301] FIG. 29 shows an embodiment to create novel "cybernetic"
sensory functions. FIG. 29 is a flowchart of an embodiment to
create a cybernetic "sixth sense" (e.g., sensing a biotoxin). The
invention summarized in FIG. 29 incorporates information associated
with the example of sensing carbon monoxide. Specific sensed
signals cause damage, to calibrate sensing and delivery of therapy
functions. For instance, exposure to carbon monoxide is dangerous,
yet this toxin is often undetected. Federal agencies in the U.S.
such as OSHA put a highest limit on long-term workplace exposure
levels of 50 ppm, with a "ceiling" of 100 ppm. Exposures of 800 ppm
(0.08%) lead to dizziness, nausea, and convulsions within 45 min,
with the individual becoming insensible within 2 hours. Clearly, an
invention to detect this toxin early and cause biofeedback through
the enciphered nervous system may have extremely practical
implications in industrial environments. Other nomograms can be
developed to identify thresholds for "safe" versus "actionable"
exposure to various stimuli including but not limited to chemicals,
biological toxins, radiation, electrical stimuli, visual stimuli
and auditory stimuli.
[0302] The invention summarized in FIG. 29 can also be used to
create novel human functionality, by using the enciphered network
to pair sensed biological or external signals to any programmed
biological or external device. It thus forms an embodiment of a
cybernetic nervous system operating in parallel with the body's
natural nervous system. The extent to which these nervous systems
are parallel or integrated will depend upon the extent to which
sensed signals are multiplexed and effector "control" signals are
combined. Examples are discussed below.
[0303] The invention outlined in FIG. 29 thus provides hitherto
unavailable programmatic control of plasticity--that is, actually
observed at some level on a regular basis in normal life. In the
realm of sensory physiology, training can enable an individual to
perceive a sensation that was previously present but not
registered/recognized. Examples include musical training to detect
tonality, or combat training to detect subtle sounds or visual
cues. In the realm of motor control, physical training can enable
an individual to use muscle groups that were previously unused. In
the realm of disease, normal "healing functions" cause undiseased
regions of the central nervous system to take over functions now
lost due to a stroke (cortical plasticity), or unaffected
peripheral nerves to take over functions of a nerve lost due to
trauma or neuropathy (expansion/plasticity of peripheral
dermatomes).
[0304] The current invention extends known interventions based upon
cortical plasticity. For instance, it is known that the dermatomal
distribution of a functioning peripheral nerve expands when an
adjacent distribution is served by a diseased nerve. In other
words, the same function can now be served by different regions of
the central or peripheral nervous system.
[0305] The invention also substantially extends normal
plasticity--by programming desired and directed regions of the body
to sense and effect functions normally reserved for other regions
of the body that are currently inaccessible (e.g., in military
combat) or unavailable (e.g., due to disease).
[0306] The invention also substantially advances normal plasticity
by integrating external sensors (e.g., for normally inaudible sound
frequencies or sensations) or devices (e.g., prosthetic limbs,
other electronic devices) into the ENS.
[0307] FIG. 29 may also include embodiments for enhancing sensory
alertness. The steps are analogous to the prior examples. The
symbolic model of scalp sensed nerve activity, e.g., in the
temporal region is empirically associated with varying alertness
levels (self-reported or monitored) in step 1710. This functional
mapping is reprogrammed using external sensed signals (step 1715)
or signals not normally associated with alertness (e.g., a specific
auditory sensed frequency), or the existing scalp signal (step
1720). In step 1725 a signal multiplexor mathematically associates
the non-associated or associated signals to program the desired
function--electrical stimulation of the scalp to increase alertness
(step 1730). Step 1735 provides an alertness monitor that can
provide an alarm or actually result in stimulated function (to
close the artificial/cybernetic feedback loop in the enciphered
nervous system) to detect and try to avoid drowsiness, coma or
toxin ingestion.
[0308] FIG. 29 depicts an embodiment to use the ENS to integrate
functionality that does not exist in nature into a personalized
biofeedback loop--in this case, detecting a toxin. Examples include
inhalation of carbon monoxide, a toxic gas that is colorless,
odorless, tasteless, and initially non-irritating, that is very
difficult for people to detect. Another example is exposure to a
biotoxin, that may not be sensed until symptoms and signs of a
disease occur hours, days weeks later. The inventive approach to
provide a "sixth sense" (step 1800) is cybernetic, since the toxin
may produce both a direct signal from a specific sensor (detected
at step 1820) and an associated biological signal (step 1830), that
are blended (or multiplexed) in the invention. Examples of a direct
signal from a dedicated sensor (element 1810) are the chemical
detection of carbon monoxide, or a biological assay for an
infective agent (viruses, bacteria, fungi). Ideally, this sensor
operates in near-real time, although this is not a requirement and
if not the case will simply provide a slower, non-real time signal.
Examples of an associated biological signal to carbon monoxide--a
toxin that is traditionally considered "unsensed"--is the specific
cherry red colorimetric change of hemoglobin from carbon monoxide
and the non-specific reduction in oxygenated hemoglobin that
results when carbon monoxide binds to oxygen binding sites.
[0309] FIG. 29 further depicts that the enciphered nervous system
of the invention forms an associative symbolic representation (step
1820) between the direct and associated biological sensed signals.
The symbolic relationship may include a direct mathematical
transform, such as a quantitative relationship of the sensed signal
to carbon monoxide or the associated biological signal of cherry
red discoloration of hemoglobin to biologically relevant
concentrations. The symbolic relationship may also use an
artificial neural network or other pattern-learning or relational
approaches to link, e.g., elevated heart rate or oxygen
desaturation to the toxin.
[0310] In FIG. 29 step 1840, signals are multiplexed in a
non-linear analytical fashion, as defined in the symbolic
representation for any specific toxin. Computer logic is then used
to control a biological or artificial effector device. Several
therapy or monitor functions can be programmed to close a
biofeedback loop. For instance, the signal from the normally
unsensed toxin can be transduced into a specific signal on a
naturally sensed "channel" (step 1860), e.g., low intensity
vibration on skin on the nostril (intuitively linked with
inhalation), or stimulation of skin over a scalp region normally
associated with deoxygenation. This latter biofeedback uses
information from training related to the individual person
(contributing to the personalized enciphered nervous system), or a
database of symbolic representations from many individuals
associating related stimuli (here, de-oxygenation) to biological
signals. This is an example of a population-based, or potentially
crowd-sourced enciphered nervous system. Another biofeedback option
is therapeutic (1860)--delivery of an antidote, by sending control
signals to a device. For carbon monoxide exposure, therapy includes
increasing oxygen concentrations (using hyperbaric oxygen in
extreme cases) and administering methylene blue.
[0311] Nomograms of the detrimental impact of sensed signals are
used to calibrate sensing and delivery of therapy functions from
the enciphered nervous system. For carbon monoxide, exposures at
100 ppm (0.01%) or greater can be dangerous to human health.
Accordingly, in the United States, Federal agencies such as OSHA
put a highest limit on long-term workplace exposure levels of 50
ppm, but individuals should not be exposed to an upper limit
("ceiling") of 100 ppm. Exposures of 800 ppm (0.08%) lead to
dizziness, nausea, and convulsions within 45 min, with the
individual becoming insensible within 2 hours. Clearly, detecting
this toxin early would have extremely practical implications in
industrial environments, for instance. Other nomograms can be
developed to identify thresholds for "safe" versus "actionable"
exposure to various stimuli including but not limited to chemicals,
biological toxins, radiation, electrical stimuli, visual stimuli
and auditory stimuli.
[0312] FIG. 30 provides another embodiment using the enciphered
network to access to the processing power of the natural nervous
system to perform an arbitrary task, such as pattern recognition
(step 1905). This embodiment of the invention is based upon 3
concepts. First, that the brain is more efficient at some tasks
than even the most powerful and well-programmed artificial
electronic computers. Pattern recognition, e.g., facial
recognition, is an excellent example that is easily accomplished by
most people yet that is suboptimal by computers even with very
sophisticated programming. Second, that the brain output from a
presented stimulation can be sensed. Third, that the brain has
unused capacity that can be accessed for this purpose. For
instance, for neural processing, only a minority is used even in
highly stressful human activities such as warrior combat (e.g., 40%
capacity used). In highly focused, non-life-or-death situations, a
minority is still used, likely 20-40%, e.g., NBA finals, SAT
testing. Therefore, there is substantial residual capacity at any
one time. This third item also presents safety limits, however, and
in the case of pattern recognition, the invention must not be used
for bioencoding images or data that would be emotionally harmful or
sensitive.
[0313] Steps 1910 and 1915 link the pattern (e.g., a face) to the
biological sensed response--for instance, activity of nerves in the
scalp over the parietal lobes of the brain, or over the forehead
indicating "recognition". This is used to create the elements of
enciphered nervous system for this task (step 1920). This will be
personalized, but can also take inputs from a multi-person
(population, crowd-sourced) encyphered nervous system. Once this
link has been made, then presentation of the pattern will result in
a "sensed" biological pattern, which is used by the multiplexer or
control logic in step 1925 to deliver a "1" (recognized) or "0"
(not recognized) to control a device (step 1930) (e.g., external
computer classifier) or stimulate the individual via a surrogate
sensation (step 1935) (e.g., vibration at the left upper arm if a
recognized pattern is detected). Uses for this invention include
pure biocomputing (pattern recognition of familiar or abstract
shapes/codes), formally encoding and enhancing memory of faces for
a particular person, and security such that only a hostile
pattern/face elicits a specific surrogate sensation or activates a
device. One other advantage of this approach over waiting for a
cognitive recognition of the pattern is that this can function as a
"background process" and/or provide faster pattern recognition.
[0314] Thus, this invention can improve and enhance function of
traditional senses, if a device is used that integrates sensors
that sense outside the normal physiological range can be used to
enhance the range of normal physiological sensation. For instance,
sensing signals in the "inaudible to humans" part of the frequency
spectrum, transducing the signal to the audible range, and
transmitting it via bony conduction using a device could be used
for private communication, encryption, recreational or other
purposes. Medically, this invention could be used to compensate for
hearing loss. This same invention with sensors of vibration could
be used to compensate for loss of this sensation in certain
neurological diseases such as peripheral neuropathy, by
transmitting this sensation to an intact sensation in a different
part of the body.
[0315] Important safety issues must be raised at this stage. While
no untoward, dangerous or otherwise undesired functionality has
been observed with this invention, certain limits must be imposed.
First, no stimulation intensity provided by the device can reach
painful or dangerous levels. Second, no sensory input can be
allowed to reach disturbing or undesired levels. Third, any sensor
or device (effector) should have acceptable and tested safety
profiles.
[0316] FIG. 31 is a block diagram of an illustrative embodiment of
a general computer system 2000. The computer system 2000 can be the
signal processing device 114 and the computing device 116 of FIG.
10. The computer system 2000 can include a set of instructions that
can be executed to cause the computer system 2000 to perform any
one or more of the methods or computer based functions disclosed
herein. The computer system 2000, or any portion thereof, may
operate as a standalone device or may be connected, e.g., using a
network or other connection, to other computer systems or
peripheral devices. For example, the computer system 2000 may be
operatively connected to signal processing device 114, analysis
database 118, and control device 120.
[0317] In operation as described in FIGS. 11-30, the modification
or enhancement of the nervous system of the body by creating and
using an enciphered functional network as described herein can be
used to enhance performance in normal individuals or restore or
treat lost function in patients.
[0318] The computer system 2000 may be implemented as or
incorporated into various devices, such as a personal computer
(PC), a tablet PC, a personal digital assistant (PDA), a mobile
device, a palmtop computer, a laptop computer, a desktop computer,
a communications device, a control system, a web appliance, or any
other machine capable of executing a set of instructions
(sequentially or otherwise) that specify actions to be taken by
that machine. Further, while a single computer system 2000 is
illustrated, the term "system" shall also be taken to include any
collection of systems or sub-systems that individually or jointly
execute a set, or multiple sets, of instructions to perform one or
more computer functions.
[0319] As illustrated in FIG. 31, the computer system 2000 may
include a processor 2002, e.g., a central processing unit (CPU), a
graphics-processing unit (GPU), or both. Moreover, the computer
system 2000 may include a main memory 2004 and a static memory 2006
that can communicate with each other via a bus 2026. As shown, the
computer system 2000 may further include a video display unit 2010,
such as a liquid crystal display (LCD), a light emitting diode such
as an organic light emitting diode (OLED), a flat panel display, a
solid state display, or a cathode ray tube (CRT). Additionally, the
computer system 2000 may include an input device 2012, such as a
keyboard, and a cursor control device 2014, such as a mouse. The
computer system 2000 can also include a disk drive unit 2016, a
signal generation device 2022, such as a speaker or remote control,
and a network interface device 2008.
[0320] In a particular embodiment, as depicted in FIG. 31, the disk
drive unit 2016 may include a computer-readable medium 2018 in
which one or more sets of instructions 2020, e.g., software, can be
embedded. Further, the instructions 2020 may embody one or more of
the methods or logic as described herein. In a particular
embodiment, the instructions 2020 may reside completely, or at
least partially, within the main memory 2004, the static memory
2006, and/or within the processor 2002 during execution by the
computer system 2000. The main memory 2004 and the processor 2002
also may include computer-readable media.
[0321] In an alternative embodiment, dedicated hardware
implementations, such as application specific integrated circuits,
programmable logic arrays and other hardware devices, can be
constructed to implement one or more of the methods described
herein. Applications that may include the apparatus and systems of
various embodiments can broadly include a variety of electronic and
computer systems. One or more embodiments described herein may
implement functions using two or more specific interconnected
hardware modules or devices with related control and data signals
that can be communicated between and through the modules, or as
portions of an application-specific integrated circuit.
Accordingly, the present system encompasses software, firmware, and
hardware implementations.
[0322] In accordance with various embodiments, the methods
described herein may be implemented by software programs tangibly
embodied in a processor-readable medium and may be executed by a
processor. Further, in an exemplary, non-limited embodiment,
implementations can include distributed processing,
component/object distributed processing, and parallel processing.
Alternatively, virtual computer system processing can be
constructed to implement one or more of the methods or
functionality as described herein.
[0323] It is also contemplated that a computer-readable medium
includes instructions or receives and executes instructions 2020
responsive to a propagated signal, so that a device connected to a
network 2024 can communicate voice, video or data over the network
2024. Further, the instructions 2020 may be transmitted or received
over the network 2024 via the network interface device 2008.
[0324] While the computer-readable medium is shown to be a single
medium, the term "computer-readable medium" includes a single
medium or multiple media, such as a centralized or distributed
database, and/or associated caches and servers that store one or
more sets of instructions. The term "computer-readable medium"
shall also include any medium that is capable of storing, encoding
or carrying a set of instructions for execution by a processor or
that cause a computer system to perform any one or more of the
methods or operations disclosed herein.
[0325] In a particular non-limiting, example embodiment, the
computer-readable medium can include a solid-state memory, such as
a memory card or other package, which houses one or more
non-volatile read-only memories. Further, the computer-readable
medium can be a random access memory or other volatile re-writable
memory. Additionally, the computer-readable medium can include a
magneto-optical or optical medium, such as a disk or tapes or other
storage device to capture carrier wave signals, such as a signal
communicated over a transmission medium. A digital file attachment
to an e-mail or other self-contained information archive or set of
archives may be considered a distribution medium that is equivalent
to a tangible storage medium. Accordingly, any one or more of a
computer-readable medium or a distribution medium and other
equivalents and successor media, in which data or instructions may
be stored, are included herein.
[0326] In accordance with various embodiments, the methods
described herein may be implemented as one or more software
programs running on a computer processor. Dedicated hardware
implementations including, but not limited to, application specific
integrated circuits, programmable logic arrays, and other hardware
devices can likewise be constructed to implement the methods
described herein. Furthermore, alternative software implementations
including, but not limited to, distributed processing or
component/object distributed processing, parallel processing, or
virtual machine processing can also be constructed to implement the
methods described herein.
[0327] It should also be noted that software that implements the
disclosed methods may optionally be stored on a tangible storage
medium, such as: a magnetic medium, such as a disk or tape; a
magneto-optical or optical medium, such as a disk; or a solid state
medium, such as a memory card or other package that houses one or
more read-only (non-volatile) memories, random access memories, or
other re-writable (volatile) memories. The software may also
utilize a signal containing computer instructions. A digital file
attachment to e-mail or other self-contained information archive or
set of archives is considered a distribution medium equivalent to a
tangible storage medium. Accordingly, a tangible storage medium or
distribution medium as listed herein, and other equivalents and
successor media, in which the software implementations herein may
be stored, are included herein.
[0328] Thus, a system and method of identifying a source of a heart
rhythm disorder, by identification of rotational of focal
activation in relation to one or more spatial elements associated
with the source of the heart rhythm disorder, have been described.
Although specific example embodiments have been described, it will
be evident that various modifications and changes may be made to
these embodiments without departing from the broader scope of the
invention. Accordingly, the specification and drawings are to be
regarded in an illustrative rather than a restrictive sense. The
accompanying drawings that form a part hereof, show by way of
illustration, and not of limitation, specific embodiments in which
the subject matter may be practiced. The embodiments illustrated
are described in sufficient detail to enable those skilled in the
art to practice the teachings disclosed herein. Other embodiments
may be utilized and derived, such that structural and logical
substitutions and changes may be made without departing from the
scope of this disclosure. This Detailed Description, therefore, is
not to be taken in a limiting sense, and the scope of various
embodiments is defined only by the appended claims, along with the
full range of equivalents to which such claims are entitled.
[0329] Such embodiments of the inventive subject matter may be
referred to herein, individually and/or collectively, by the term
"invention" merely for convenience and without intending to
voluntarily limit the scope of this application to any single
invention or inventive concept if more than one is in fact
disclosed. Thus, although specific embodiments have been
illustrated and described herein, it should be appreciated that any
arrangement calculated to achieve the same purpose may be
substituted for the specific embodiments shown. This disclosure is
intended to cover any and all adaptations or variations of various
embodiments. Combinations of any of the above-described
embodiments, and other embodiments not specifically described
herein, may be used and are fully contemplated herein.
[0330] The Abstract is provided to comply with 37 C.F.R. .sctn.
1.72(b) and will allow the reader to quickly ascertain the nature
and gist of the technical disclosure. It is submitted with the
understanding that it will not be used to interpret or limit the
scope or meaning of the claims.
[0331] In the foregoing description of the embodiments, various
features are grouped together in a single embodiment for the
purpose of streamlining the disclosure. This method of disclosure
is not to be interpreted as reflecting that the claimed embodiments
have more features than are expressly recited in each claim.
Rather, as the following claims reflect, inventive subject matter
lies in less than all features of a single disclosed embodiment.
Thus the following claims are hereby incorporated into the
Description of the Embodiments, with each claim standing on its own
as a separate example embodiment.
* * * * *
References