U.S. patent application number 15/443956 was filed with the patent office on 2017-06-15 for method and system for combining physiological and machine information to enhance function.
The applicant listed for this patent is Incyphae Inc.. Invention is credited to Sanjiv M. Narayan, Ruchir Sehra.
Application Number | 20170164893 15/443956 |
Document ID | / |
Family ID | 54056291 |
Filed Date | 2017-06-15 |
United States Patent
Application |
20170164893 |
Kind Code |
A1 |
Narayan; Sanjiv M. ; et
al. |
June 15, 2017 |
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.; (La
Jolla, CA) ; Sehra; Ruchir; (Scottsdale, AZ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Incyphae Inc. |
Scottsdale |
AZ |
US |
|
|
Family ID: |
54056291 |
Appl. No.: |
15/443956 |
Filed: |
February 27, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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PCT/US2015/047820 |
Aug 31, 2015 |
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15443956 |
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PCT/US2015/046819 |
Aug 25, 2015 |
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PCT/US2015/047820 |
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PCT/US2015/046819 |
Aug 25, 2015 |
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PCT/US2015/046819 |
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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 5/0205 20130101;
A61B 5/0492 20130101; A61B 5/14539 20130101; A61B 5/0402 20130101;
A61B 5/04888 20130101; A61B 5/024 20130101; A61B 5/0531 20130101;
A61B 5/4836 20130101; A61B 5/0488 20130101; A61B 5/01 20130101;
A61B 5/0476 20130101; A61B 5/40 20130101; A61B 5/7267 20130101;
A61F 2/72 20130101; A61B 5/04001 20130101; A61B 2560/0242 20130101;
A61B 5/6877 20130101; A61B 5/0408 20130101; A61B 5/0816
20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/0492 20060101 A61B005/0492; A61B 5/0205 20060101
A61B005/0205; A61B 5/0408 20060101 A61B005/0408; A61B 5/04 20060101
A61B005/04; A61B 5/0488 20060101 A61B005/0488 |
Claims
1. A method for interacting with the human body, the method
comprising: 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; using signal
processing algorithms to tailor signatures to a given bodily
function using an enciphered functional network in an individual
patient to determine one or more effector responses needed to
control a bodily task in the individual patient; 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.
2-3. (canceled)
4. The method of claim 1 wherein the bodily signals associated with
a biological function are selected from the group consisting of
central or peripheral nervous system signals, autonomic nervous
system signals, muscular activity signals, non-neurologic
physiological signals, galvanic skin signals and combinations
thereof.
5. The method of claim 1, wherein the bodily task is a biological
function.
6. The method of claim 1, wherein the bodily task is control of
activity of a machine external to the body.
7. The method of claim 1, wherein the bodily task is control of
activity of a machine on or inside the body.
8. The method of claim 1, wherein the bodily task is a combination
of an external machine and biological function.
9. The method of claim 1, wherein the enciphered functional network
is represented by symbolic code.
10. The method of claim 9, wherein the symbolic code is a
cypher.
11. The method of claim 1, wherein the effector signal directs one
or more of a mechanical, an electrical and a computational
device.
12. The method of claim 1, wherein the detecting and the delivering
comprise different regions of the human body.
13. The method of claim 1, wherein the detecting and the delivering
comprise identical regions of the human body.
14. The method of claim 1, wherein the controlling comprises
treating a biological disease or a biological condition.
15. The method of claim 1, wherein the controlling comprises
enhancing the performance of a bodily task directly.
16. The method of claim 1, wherein the controlling comprises
enhancing the performance bodily task using an external
machine.
17-139. (canceled)
140. A system for interacting with the human body, the system
comprising: a processor; a memory storing instructions that, when
executed by the processor, performs operations comprising:
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; using signal
processing algorithms to tailor signatures to a given bodily
function using an enciphered functional network in an individual
patient to determine one or more effector responses needed to
control a bodily task in the individual patient; 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.
141-172. (canceled)
173. The method of claim 1, wherein the bodily function is selected
from the group consisting of alertness, biological disease,
breathing, cardiac muscular movement, chronic obstructive pulmonary
disease, cognition, driving, memory, mental alertness, mental
performance, mood, overall mental performance, patient moving a
combined natural/cybernetic limb, physically moving an object,
purposeful communication using movement, reading with glasses,
response to heart failure, response to obesity, sense of hearing,
sense of smell, sense of touch, sense of vision, sensory-motor
activities, skeletal muscular movement, sleep, sleep apnea, sleep
control, sleep disordered breathing, sleep-breathing conditions,
using remote control unit and combinations thereof.
174. The method of claim 1, wherein said one or more sensors is
selected from the group consisting of: (i) solid physical sensors
selected from the group consisting of FINE, ECG-electrical sensors,
EEG-electrical sensors and combinations thereof, (ii) non-solid
physical sensors selected from the group consisting of
electrostatic creams, sensors for bioimpedance, piezoelectric film
sensors, printed circuit sensors, photosensitive film,
thermosensitive film and combinations thereof, (iii)
external-oriented sensors selected from the group consisting of
video sensors, infrared sensors, temperature sensors, gas sensors
and combinations thereof, and (iv) combinations thereof.
175. The method of claim 1, wherein said sensor is a biological
sensor that senses at least one selected from the group consisting
of photosensitive sensors, galvanometers, transcutaneous or
invasive nerve activity (neural electrical activity), muscle
electrical activity (myopotentials), pressure detectors, thermal
detectors, chemical detectors, mechanical activity
(mechanoreceptors), body pH, skin pH, mouth pH, gastrointestinal
pH, genitourinary tract pH, enzymatic profile, DNA profile, heart
rate, and ventilating (breathing) rate.
176. The method of claim 1, wherein said sensor is an external
sensor that senses biological signals from the nervous system, from
another individual's nervous system or from a database of
signals.
177. The method of claim 1, wherein said sensor is an external
sensor that provides information selected from the group consisting
of pressure, physical movement, temperature, chemical, sound within
the normal human physiological range, sound outside the normal
human physiological range, sound within the physiological range of
animals, electromagnetic radiation in the visible spectrum,
electromagnetic radiation in the invisible spectrum, gamma
radiation, X-rays, radiowaves, toxins, carbon monoxide, excessive
carbon dioxide, radiation, alpha radiation, beta radiation,
biotoxins, toxins of E. coli, and anthrax.
178. The system of claim 140, wherein the bodily function is
selected from the group consisting of alertness, biological
disease, breathing, cardiac muscular movement, chronic obstructive
pulmonary disease, cognition, driving, memory, mental alertness,
mental performance, mood, overall mental performance, patient
moving a combined natural/cybernetic limb, physically moving an
object, purposeful communication using movement, reading with
glasses, response to heart failure, response to obesity, sense of
hearing, sense of smell, sense of touch, sense of vision,
sensory-motor activities, skeletal muscular movement, sleep, sleep
apnea, sleep control, sleep disordered breathing, sleep-breathing
conditions, using remote control unit and combinations thereof.
179. The system of claim 140, wherein said one or more sensors is
selected from the group consisting of: (i) solid physical sensors
selected from the group consisting of FINE, ECG-electrical sensors,
EEG-electrical sensors and combinations thereof, (ii) non-solid
physical sensors selected from the group consisting of
electrostatic creams, sensors for bioimpedance, piezoelectric film
sensors, printed circuit sensors, photosensitive film,
thermosensitive film and combinations thereof, (iii)
external-oriented sensors selected from the group consisting of
video sensors, infrared sensors, temperature sensors, gas sensors
and combinations thereof, and (iv) combinations thereof.
180. The system of claim 140, wherein said sensor is a biological
sensor that senses at least one selected from the group consisting
of photosensitive sensors, galvanometers, transcutaneous or
invasive nerve activity (neural electrical activity), muscle
electrical activity (myopotentials), pressure detectors, thermal
detectors, chemical detectors, mechanical activity
(mechanoreceptors), body pH, skin pH, mouth pH, gastrointestinal
pH, genitourinary tract pH, enzymatic profile, DNA profile, heart
rate, and ventilating (breathing) rate.
181. The system of claim 140, wherein said sensor is an external
sensor that senses biological signals from the nervous system, from
another individual's nervous system or from a database of
signals.
182. The system of claim 140, wherein said sensor is an external
sensor that provides information selected from the group consisting
of pressure, physical movement, temperature, chemical, sound within
the normal human physiological range, sound outside the normal
human physiological range, sound within the physiological range of
animals, electromagnetic radiation in the visible spectrum,
electromagnetic radiation in the invisible spectrum, gamma
radiation, X-rays, radiowaves, toxins, carbon monoxide, excessive
carbon dioxide, radiation, alpha radiation, beta radiation,
biotoxins, toxins of E. coli, and anthrax.
183. The system of claim 140, wherein the processor is selected
from the group consisting of a central processing unit (CPU), a
graphics-processign unit (GPU) and a combination thereof.
184. The system of claim 140, wherein the bodily signals associated
with a biological function are selected from the group consisting
of central or peripheral nervous system signals, autonomic nervous
system signals, muscular activity signals, non-neurologic
physiological signals, galvanic skin signals and combinations
thereof.
185. The system of claim 140, wherein the bodily task is selected
from the group consisting of a biological function, control of
activity of a machine external to the body, control of activity of
a machine on or inside the body, a combination of an external
machine and biological function and combinations thereof.
186. The system of claim 140, wherein the enciphered functional
network is represented by symbolic code.
187. The system of claim 186, wherein the symbolic code is a
cypher.
Description
FIELD
[0001] 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.
[0002] 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).
BRIEF DISCUSSION OF RELATED ART
[0003] 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.
[0004] 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.
[0005] 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.
[0006] 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.
[0007] 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.
[0008] 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.
[0009] 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.
[0010] 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.
[0011] 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.
[0012] 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.
[0013] 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.
[0014] 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.
[0015] 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.
[0016] 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).
[0017] 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.
[0018] 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.
[0019] 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.
[0020] 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.
[0021] 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.
[0022] 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.
[0023] 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.
[0024] 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.
[0025] Traditional therapies have also not typically been effective
for managing central sleep apnea, other cognitive or performance
functions, alertness, heart failure or obesity.
[0026] 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.
[0027] 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.
[0028] 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.
[0029] 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
[0030] The invention is 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.
[0031] For the purposes of this disclosure, the following
definitions apply.
[0032] 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.
[0033] 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.
[0034] 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.
[0035] 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.
[0036] 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.
[0037] 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.
[0038] Effector signal is the signal delivered by the invention to
the effector to produce the effector response.
[0039] 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.
[0040] 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.
[0041] 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. 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).
[0042] 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.
[0043] Encipher is defined as the process of coding
information.
[0044] 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.
[0045] 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.
[0046] Extremity of the body is defined as limbs and associated
structures of the body including arms, legs, hands, feet, fingers,
toes, and subsegments thereof.
[0047] 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.
[0048] 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.
[0049] 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.
[0050] 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.
[0051] 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.
[0052] 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.
[0053] "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.
[0054] 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.
[0055] Other biological terms take their standard definitions, such
as heart failure, tidal volume, sleep apnea, obesity and so on.
[0056] This invention creates an enciphered functional network. The
potential number of uses of this invention are broad.
[0057] In one 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.
[0058] 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.
[0059] 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.
[0060] 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.
[0061] 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.
[0062] 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.
[0063] 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.
[0064] 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.
[0065] 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.
[0066] 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.
[0067] 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.
[0068] 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.
[0069] 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.
[0070] 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.
[0071] 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.
[0072] 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.
[0073] 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.
[0074] 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.
[0075] 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.
[0076] 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.
[0077] 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.
[0078] 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.
[0079] 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.
[0080] 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 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.
[0081] 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.
[0082] 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.
[0083] 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.
[0084] 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 continous positive
airway pressure or nerve stimulation are often delivered
empirically, continuously or in predetermined fashions without
adaptive algorithms to tailor therapy.
[0085] 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.
[0086] 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.
[0087] 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.
[0088] 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.
[0089] 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.
[0090] 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.
[0091] 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.
[0092] 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.
[0093] 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.
[0094] 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 systerna 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.
[0095] 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.
[0096] 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.
[0097] 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.
[0098] 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.
[0099] 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.
[0100] 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).
[0101] 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-brain-controlled-prosthetic-a-
rm-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.
[0102] 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.
[0103] 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.
[0104] 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).
[0105] 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.
[0106] 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.
[0107] 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.
[0108] 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).
[0109] 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
[0110] Some embodiments are illustrated by way of example and not
limitation in the figures of the accompanying drawings, in
which:
[0111] FIG. 1 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.
[0112] FIG. 2 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.
[0113] FIG. 3 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.
[0114] FIG. 4 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.
[0115] FIG. 5 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.
[0116] FIG. 6 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.
[0117] FIG. 7 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. 1, 12-18) that
provide a variety of extrinsic or artificial signatures (FIGS.
12-18).
[0118] FIG. 8 shows an example of an embodiment of sensed
signatures in sleep disordered breathing.
[0119] FIG. 9 shows an example of an embodiment of effector
locations for sleep disordered breathing.
[0120] FIG. 10 shows an example of an embodiment of sensed
signature for heart failure.
[0121] FIG. 11 shows an example of an embodiment of sensed
signature of body response to obesity.
[0122] FIG. 12 shows an example of an embodiment of sensed
signatures for other conditions.
[0123] FIG. 13 shows an enciphered (symbolic) network model for
physiology of sleep-disordered breathing.
[0124] FIG. 14 shows enhancement of body function using enciphered
network.
[0125] FIG. 15 shows cybernetic enhancement of body function using
enciphered functional network.
[0126] FIG. 16 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).
[0127] FIG. 17 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).
[0128] FIG. 18 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.
[0129] FIG. 19 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.
[0130] FIG. 20 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.
[0131] FIG. 21 shows computer hardware for machine learning.
DETAILED DESCRIPTION
[0132] A system and method for enhancing and modifying complex
functions 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.
[0133] 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.
[0134] FIG. 1 illustrates an example system to modify and enhance
complex body functions in a human being. Specifically, the example
system 100 is configured to access external signals from biological
sensors 104 and from external sensors 110.
[0135] The biological sensors 104 can sense biological signals,
from an individual, from another individual, or from a database of
signals 118. The biological sensors 104 can be wearable.
[0136] External sensors 110 can sense biological signals, from an
individual, from another individual or from a database of signals
118. 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.
[0137] 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 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).
[0138] External sensors 110 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).
[0139] External sensors 110 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.
[0140] In FIG. 1, signals are delivered either wirelessly or via
connected communication to a signal processing device 114
functioning with a computing device 116 that has access to an
analysis database 118. The computing device 116 and signal
processing device 114 communicate with a control device 120, which
in turn controls a biological device 108 or an external device 112.
The biological device 108 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).
[0141] FIG. 2 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 150 one can see
the entire functional network domain for a particular bodily
function, such as sleep or breathing. At 155 are illustrated
sensors 1, 2, . . . n that are used to provide sensed signatures
160 for this functional domain. The enciphered functional network
165 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. 3) depending on
its learned or programmed behaviors. Many forms of analysis can be
performed as discussed below. Item 170 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 175. A key element of the invention is
interconnectivity and links between each element within/with the
enciphered functional network, indicated by double arrows.
[0142] FIG. 3 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.
[0143] In FIG. 3, a body function is represented by nervous system
220 and non-nervous system (non-neural) 260 networks. The networks
220, 260 comprise respective functional domains 230, 270, defined
by signatures 240, 280 based on a variety of sensors. This produces
nerve and non-nerve signatures for the body function, which can be
normal 250 and abnormal 290--or desired 250 and undesired 290. It
should be noted that the networks can interact via interactions 225
and signatures may be inter-related by relationships 245.
[0144] 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.
[0145] 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.
[0146] Machine learning nominally links signatures with normal
function 250 in order to create a patient specific range to detect
abnormal function 290 as outliers. In practice, the best results
are obtained when the machine learning algorithms perform repeated
pattern classification interactions 255 between sensed signatures
for normal 250 and abnormal 290 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 235.
[0147] In FIG. 3, 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 215) 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.
[0148] FIG. 4 provides detail of signatures sensed 310 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 315 would
typically represent the sensing location 320, patterns of activity
325 (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 330 (e.g., the
fundamental or "dominant" frequency of a spectrum or first peak on
an autocorrelation function).
[0149] 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.
[0150] Non-nerve signatures 335 represent other modalities 340 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 345, rate 350 and temporal patterns over time 355.
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.
[0151] The network of sensed signatures exemplified in FIG. 4
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.
[0152] 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.
[0153] FIG. 5 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 400 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 410 may thus lie in the peripheral nervous or
in the central nervous system 420, 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 430.
Non-nerve domains can be modified 440 in many ways including
vibratory stimulation via a piezoelectric device to stimulate a
muscle, infrared heat to reduce muscle spasm to modulate the domain
450 and network 460 to modify the bodily function 430. Notably,
modification is individually tailored via personalized sensory
signatures and the enciphered network.
[0154] Modulation of nerve-related domains 410 can be linked to
modulation of non-nervous domains by modulation connection 415.
Moreover, the central and peripheral nervous network 420 can be
linked to the non-nervous system physiologic network by network
connection 425.
[0155] FIG. 6 indicates several potential body 500 locations for
sensing signatures and modifying different functional domains.
Bodily functions can be measured by sensor 505 and/or modified by
effector 510 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. 6 indicates sensor locations on the body 500
to detect signatures of the nervous 535, cardiovascular 540,
pulmonary 540, gastrointestinal 545, genitourinary 550, skin 550
and other domains. Body functions measured and/or modified by the
enciphered functional network include sleep and central sleep apnea
515, cognitive performance 520 such as alertness, obstructive sleep
apnea 525, and the bodily response to obesity 530. 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.
[0156] FIG. 7 illustrates an example of a body sensor 600,
comprising sensor element 605, power source 610, processing
components 615, nonvolatile storage 620 (e.g., E2PROM, powered
RAM), communication element 625 on a structural platform 630.
Several types of sensor elements are illustrated Biological sensors
include, but are not limited to, photosensitive sensors 640 to
detect skin reflectance (indicating oxygenated hemoglobin, and
perfusion), galvanometers 650 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 660 (to
detect pressure, e.g., weight, mechanical joint movement or
position), thermal detectors 670 to detect temperature (a measure
of metabolic activity and other disease states), and chemical
detectors 680 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).
[0157] The invention can also use external sensors (FIGS. 1, 12-18)
that provide a variety of extrinsic or artificial signatures (FIGS.
12-18) to provide cybernetic sensor inputs or effectors to the
enciphered functional network.
[0158] FIG. 8 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. 8, 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.
[0159] FIG. 9 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 800 is interfaced with effector devices 810, tailored to
each modality. For sleep apnea 820 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. 7).
Accordingly, controlled negative pressure in the lower extremities
840 can reverse this rostral fluid accumulation. Direct stimulation
of pro-sleep centers by other methods 850 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 860 to mimic the
somnorific impact of massage, or stimulation of post-prandial
satiety sensors 870 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.
[0160] FIG. 10 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. 10, 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.
[0161] FIG. 11 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. 11, 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.
[0162] FIG. 12 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. 12, 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.
[0163] FIG. 13 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).
[0164] More specifically, FIG. 13 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.
[0165] 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 amydala 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.
[0166] The schematic shown in the left panel of FIG. 13 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.
[0167] In the right panel of FIG. 13, 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.
[0168] The computational element 1255 forms a symbolic relationship
between sensed signals and biological function (e.g., elements
250-290 in FIG. 1). 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.
[0169] The symbolic relationship of the enciphered network in FIG.
13 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.
[0170] 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.
[0171] 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.
[0172] In FIG. 13, 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.
[0173] Moreover, FIG. 13 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.
[0174] FIG. 14 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).
[0175] Item 1310 applies the symbolic model of the enciphered
network, as identified in FIG. 8 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.
[0176] 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.
[0177] In FIG. 14, 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".
[0178] In FIG. 14, step 1325 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 1325. 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.
[0179] Furthermore, the invention can 1325 artificially generate
signals needed to stimulate the muscle. 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.
[0180] In FIG. 14, 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. 14. In FIG. 13, 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.
[0181] In FIG. 14, 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.
[0182] 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.
[0183] 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.
[0184] In FIG. 14, step 1350 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.
[0185] FIG. 15 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.
[0186] FIG. 15 is an embodiment in which intrinsic biological
signals and extrinsic non-biological signals are sensed (step
1400). The enciphered network does not simply map learned function
to sensed signals, but instead extrapolates from learned functions
to create novel function 1410. The enciphered representation of the
body function to sensed signals is extended to a personalized
network in step 1420 via machine learning. This involves a series
of steps, including 1430 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 1440 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. 12), 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).
[0187] Step 1450 in FIG. 15 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.
[0188] Several embodiments exist. In step 1460, 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.
[0189] FIG. 15 step 1470 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. 14 (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.
[0190] In FIG. 15 step 1480, 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.
[0191] In FIG. 15 step 1490, 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).
[0192] In FIG. 15 step 1490, novel functionality can be provided
for motor function (i.e., previously unavailable movements) or
sensory function (i.e., a cybernetic 6th 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.
[0193] FIG. 16 illustrates an embodiment of motor function
controlled by the enciphered network. The Flowchart in FIG. 16
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).
[0194] 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.
[0195] FIG. 17 shows an embodiment of enhancing sensory function
via the enciphered network. FIG. 17 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.
[0196] FIG. 18 depicts an embodiment of the invention to transform
sensory function. FIG. 18 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.
[0197] FIG. 19 shows an embodiment to create novel "cybernetic"
sensory functions. FIG. 19 is a flowchart of an embodiment to
create a cybernetic "sixth sense" (e.g., sensing a biotoxin). The
invention summarized in FIG. 19 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.
[0198] The invention summarized in FIG. 19 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.
[0199] The invention outlined in FIG. 19 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).
[0200] 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.
[0201] 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).
[0202] 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.
[0203] FIG. 19 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.
[0204] FIG. 19 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.
[0205] FIG. 19 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 quantitive 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.
[0206] In FIG. 19 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.
[0207] 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.
[0208] FIG. 20 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.
[0209] 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.
[0210] 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.
[0211] 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.
[0212] FIG. 21 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.
1. 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.
[0213] In operation as described in FIGS. 1-20, 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.
[0214] 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.
[0215] As illustrated in FIG. 21, 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.
[0216] In a particular embodiment, as depicted in FIG. 21, 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.
[0217] 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.
[0218] 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.
[0219] 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.
[0220] 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.
[0221] 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.
[0222] 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.
[0223] 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.
[0224] 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.
[0225] 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.
[0226] 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.
[0227] 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