U.S. patent application number 10/778306 was filed with the patent office on 2004-11-18 for method and apparatus for affecting the autonomic nervous system.
Invention is credited to Kullok, Saul, Kullok, Yossi.
Application Number | 20040230252 10/778306 |
Document ID | / |
Family ID | 46300870 |
Filed Date | 2004-11-18 |
United States Patent
Application |
20040230252 |
Kind Code |
A1 |
Kullok, Saul ; et
al. |
November 18, 2004 |
Method and apparatus for affecting the autonomic nervous system
Abstract
A method and apparatus for affecting the autonomic nervous
system of a subject using stimuli based on separate analysis of the
sympathetic and/or parasympathetic branches of the subject's
autonomic nervous system. The present invention also relates to a
method and apparatus for affecting the autonomic nervous system,
wherein stimuli is applied in coordination with cyclical activities
of the subjects body such as respiration or cardiac cycle.
Inventors: |
Kullok, Saul; (Jerusalem,
IL) ; Kullok, Yossi; (Jerusalem, IL) |
Correspondence
Address: |
STERNE, KESSLER, GOLDSTEIN & FOX PLLC
1100 NEW YORK AVENUE, N.W.
WASHINGTON
DC
20005
US
|
Family ID: |
46300870 |
Appl. No.: |
10/778306 |
Filed: |
February 17, 2004 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10778306 |
Feb 17, 2004 |
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09715046 |
Nov 20, 2000 |
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09715046 |
Nov 20, 2000 |
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09176566 |
Oct 21, 1998 |
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Current U.S.
Class: |
607/48 |
Current CPC
Class: |
A61M 2021/0022 20130101;
A61M 2021/0066 20130101; A61N 2005/0663 20130101; A61M 2021/0027
20130101; A61M 2021/0044 20130101; A61M 2021/0016 20130101; A61M
21/00 20130101 |
Class at
Publication: |
607/048 |
International
Class: |
A61N 001/18 |
Claims
1-65. (canceled)
66. A method for non-invasive treatment of a condition by affecting
an autonomic nervous system of a human subject having a heart cycle
wherein the method comprises: applying a plurality of stimuli to
the human subject wherein the stimuli are of a same stimulus type
and have a same stimulus quality modulated by a stimulus parameter
and wherein the plurality of stimuli comprise: a first stimulus
applied at a time T1 and having a stimulus parameter S1; a second
stimulus applied at a time T2 and having a stimulus parameter S2;
and a third stimulus applied at a time T3 and having a stimulus
parameter S3; monitoring the heart cycle of the human subject
during a first time period to generate first heart data wherein the
first time period ends after T1; analyzing the first heart data to
assess a first activity level (A1) of the autonomic nervous system;
calculating S2 based on A1; monitoring the heart cycle of the human
subject during a second time period to generate second heart data
wherein the second time period ends after T2; analyzing the second
heart data to assess a second activity level (A2) of the autonomic
nervous system; and calculating S3 based on A2.
67. The method of claim 66, wherein the stimulus type is a sensory
stimulus type.
68. The method of claim 66, wherein the stimulus type is selected
from the group consisting of: light, sound, taste, smell,
temperature, balance and touch.
69. The method of claim 68, wherein the stimulus quality is a
quality selected from the group consisting of: of brightness,
intensity, loudness, color, wavelength, frequency, flavor, aroma
and pitch.
70. The method of claim 68, wherein the plurality of stimuli are
synchronized to the heart cycle of the human subject.
71. The method of claim 70, wherein each of analyzing the first
heart data and analyzing the second heart data comprises assessing
a heart rate variability.
72. The method of claim 71, wherein assessing a heart rate
variability comprises performing frequency domain analysis.
73. The method of claim 72, wherein performing frequency domain
analysis is accomplished using at least one method selected form
the group consisting of: Fast Fourier Transform analysis,
autoregressive analysis and wavelet analysis.
74. The method of claim 73, wherein each of A1 and A2 is calculated
so as to reflect the activity of a same branch of the autonomic
nervous system selected from the group consisting of: the
parasympathetic branch and sympathetic branch.
75. The method of claim 71, wherein the first time period overlaps
the second time period and the second time period overlaps the
third time period.
76. A method for affecting the functioning of an autonomic nervous
system of a human subject comprising: applying a first stimulus
commencing at a first time (T1) to the human subject wherein the
stimulus has a stimulus characteristic determined by a first value;
monitoring a cyclic activity of the human subject during a first
time period to generate first activity data wherein the first time
period ends after T1; analyzing the first activity data to assess a
first activity level of the autonomic nervous system; calculating a
second value based on the first activity level; applying a second
stimulus commencing at a second time (T2) having a stimulus
characteristic determined by the second value to the human subject;
monitoring the cyclic activity of the human subject during a second
time period to generate second activity data wherein the second
time period ends after T2; analyzing the second activity data to
assess a second activity level of the autonomic nervous system;
calculating a third value based on the second activity level; and
applying a third stimulus commencing at a third time (T3) having a
stimulus characteristic defined by the third value to the human
subject.
77. The method of claim 76, wherein each of the first stimulus,
second stimulus and third stimulus is a sensory stimulus of a same
type.
78. The method of claim 77, wherein the type is selected from the
group consisting of: light, sound, taste, smell, temperature,
balance and touch.
79. The method of claim 78, wherein each stimulus characteristic is
a stimulus characteristic of a same type selected from the group
consisting of: of brightness, intensity, loudness, color,
wavelength, frequency, and pitch.
80. The method of claim 79, wherein each stimulus is of a same type
selected from the group consisting of: light and sound.
81. The method of claim 78, wherein T1, T2 and T3 are synchronized
to the cyclic activity of the human subject.
82. The method of claim 81, wherein the cyclic activity is a
cardiopulmonary activity.
83. The method of claim 82, wherein the cyclic activity is
breathing.
84. The method of claim 83, wherein each of the first activity
level and second activity level is calculated so as to reflect the
activity of a branch of the autonomic nervous system selected from
the group consisting of: the parasympathetic branch and sympathetic
branch.
85. The method of claim 76, wherein each of the first activity
level and second activity level is calculated so as to reflect the
activity of any one branch of the autonomic nervous system selected
from the group consisting of: the parasympathetic branch and
sympathetic branch.
86. The method of claim 85, wherein the first time period overlaps
the second time period and the second time period overlaps the
third time period.
87. Apparatus for treating a condition by affecting the autonomic
nervous system of a human subject comprising: stimuli applicator
means for applying a first stimulus having a stimulus
characteristic determined by a first parameter (S1) to the human
subject at a first time (T1), applying a second stimulus having a
stimulus characteristic determined by a second parameter (S2) to
the human subject at a second time (T2) and applying a stimulus
having a stimulus characteristic determined by a third parameter
(S3) to the human subject at a third time (T3); activity monitor
means for monitoring a cyclic activity of the human subject during
a first period ending after T1 to generate first activity data and
monitoring the cyclic activity of the human subject during a second
time period ending after T2 to generate second activity data; and
stimuli modulator means for analyzing the first activity data to
assess a first performance (A1) of the autonomic nervous system
during the first time period and analyzing the second activity data
to assess a second performance (A2) of the autonomic nervous system
during the second period; and wherein the stimuli modulator means
calculates S2 based on A1 and calculates S3 based on A2.
88. The method of claim 87, wherein each of the first stimulus,
second stimulus and third stimulus is a sensory stimulus of a same
type.
89. The method of claim 87, wherein each of the first stimulus,
second stimulus and third stimulus is a stimulus of a same type
selected from the group consisting of: light, sound, taste, smell,
temperature, balance and touch.
90. The apparatus of claim 89, wherein each stimulus characteristic
is a stimulus characteristic of a same type selected from the group
consisting of: of brightness, intensity, loudness, color,
wavelength, flavor, aroma, frequency, and pitch.
91. The apparatus of claim 90, wherein each stimulus is of a same
type selected from the group consisting of: light and sound.
92. The apparatus of claim 89, wherein the stimuli applicator means
comprises means for synchronizing T1, T2 and T3 to the cyclic
activity of the human subject.
93. The apparatus of claim 92, wherein the cyclic activity is a
cardiopulmonary activity.
94. The apparatus of claim 93, wherein the cyclic activity is
breathing.
95. The apparatus of claim 92, wherein A1 and A2 are calculated so
as to reflect the performance of any one branch of the autonomic
nervous system selected from the group consisting of: the
parasympathetic branch and sympathetic branch.
96. The apparatus of claim 95, wherein the stimuli modulator means
comprises means for performing frequency domain analysis of the
first activity data and second activity data.
97. The apparatus of claim 92, where the cyclic activity is
cardiovascular activity.
98. The apparatus of claim 97, wherein the activity monitor means
comprises an ECG monitor.
99. The apparatus of claim 98, wherein the stimuli modulator means
comprises means for performing heart rate variability analysis of
the first activity data and second activity data.
100. The apparatus of claim 99, wherein the stimuli applicator
means comprises an audiovisual display device.
Description
FIELD OF INVENTION
[0001] The present invention relates generally to a method and
apparatus for affecting a subject's health or condition by using
information regarding the sympathetic and/or parasympathetic
branches of the subject's autonomic nervous system to apply and/or
modulate stimuli to the subject. The present invention also relates
to a method and apparatus for affecting the autonomic nervous
system, wherein stimuli is applied in coordination with cyclical
activities of the subject's body.
BACKGROUND
[0002] The autonomic nervous system (ANS) and its role in health
and pathology is a field of medicine that has been explored and
written about at great length. There are also several prior art
methods and devices that use the concept of applying sensory
stimuli to a subject's body to affect the subject's health or
condition.
[0003] I. Application of Stimuli to Affect the Body
[0004] The prior art includes methods and devices for applying
stimuli to a patient's body. For example, U.S. Pat. No. 5,577,990
to Widjaja et al. discloses a device that directs light and sound
toward a patient, apparently eliciting a relaxation response from
the patient. It is also known in the art to combine stimuli with
feedback from the body. For example, U.S. Pat. No. 5,562,719 to
Lopez-Claros discloses a method and apparatus for treating
disorders such as Seasonal Affective Disorder by preferentially
directing light therapy to the non-dominant hemisphere of the
brain. In addition, U.S. Pat. No. 4,289,121 to Kupriyanovich
discloses a method and device for controlling the functional state
of the central nervous system using audio and light signals applied
according to the body's biorhythms that correspond to a stable
state of the central nervous system. This reference describes
modulating frequencies depending on the electroencephalogram (EEG),
electrocardiogram (ECG), or the measured respiration rate of the
patient, wherein the amplitude or rhythmic signals correspond to
the volume of sound and brightness of light. Kupriyanovich also
suggests that the device include a feedback system to automatically
vary the illumination in response to certain changes in the
patient's vital signs, including the patient's pulse rate,
temperature, and respiratory rate. A patient's vital signs,
however, do not provide complete information about the autonomic
nervous system. The same vital sign reading for one patient may
represent different levels of sympathetic and parasympathetic
activity. Therefore, the complexity or the various dimensions of
the autonomic nervous system activity is not fully reflected in a
patient's vital sign measurements or in the patient's
biorhythms.
[0005] In addition, in the prior art methods and devices, stimuli
which generally affect the sympathetic branch differently than the
parasympathetic branch, are applied without regard to the autonomic
nervous system balance and activity. For example, U.S. Pat. No.
5,076,281 to Gavish discloses a biorhythm modulator, which produces
music-like sound pattern signals based on a patient's biorhythmic
activity. Although Gavish notes that certain activities of the body
are associated with the sympathetic nervous system, the rhythm of
the sound synthesized patterns are based simply on a patient's
monitored respiration rate, and not on separate analyses of the
sympathetic and parasympathetic branches of the autonomic nervous
system.
[0006] The prior art also includes stimulation treatments and
biofeedback treatments that involve the patient's cognitive
awareness and involvement in the treatment. These treatments are
also known to include adjustment of the stimuli through trial and
error. In the Kupriyanovich reference described above, the patient
chooses the initial light and audio signals. However, a patient's
subjective feelings do not accurately reflect the complex
interactions of the patient's autonomic nervous system.
[0007] It is known that, generally, colors ranging from green to
blue or violet are calming colors, and that these colors have the
effect of stimulating the parasympathetic branch. It is also known
that, generally, colors ranging from red to yellow are rousing
colors, and that these colors have the effect of stimulating the
sympathetic branch. Hospitals tend to incorporate greens and blues
in their interior color scheme in order to calm and soothe
patients, whereas fast food restaurants are typically red, yellow,
and orange in order to move customers in and out of the restaurants
quickly. In addition, it is known that increasing the brightness or
intensity of a color increases its stimulatory effect.
[0008] It is also known that, generally, sounds having a pitch
below 500 cycles or Hz tend to have a calming effect, whereas
sounds having a pitch above 500 cycles tend to have a rousing
effect. In addition, it is known that the louder the sound, the
greater the stimulation.
[0009] As is apparent from the discussion above, known methods or
devices generally do not provide for treatment of a patient based
on the full range of information which can be ascertained from the
condition of the autonomic nervous system. For example, prior art
treatment methods or devices generally do not take advantage of
independent or separate analyses of the sympathetic and
parasympathetic branches of the autonomic nervous system.
[0010] II. The Autonomic Nervous System and Heart Rate
Variability
[0011] A. Nervous System
[0012] The nervous system comprises the central nervous system and
the peripheral nervous system. The central nervous system comprises
the brain and spinal cord, and the peripheral nervous system
comprises a network of nerves that connects the brain and spinal
cord to the rest of the body.
[0013] The brain, which is the site of cognitive awareness and the
control center for the rest of the body, comprises the cerebrum,
the brain stem, and the cerebellum. The brain coordinates the
ability to move, touch, taste, smell, hear, and see. The cerebrum
regulates a variety of voluntary activities of the body, including
speech, thought, planning, and initiating communication or
action.
[0014] A variety of critical body functions are automatically
regulated by the brain stem. These functions include adjusting
posture, regulating breathing, swallowing, and heart rate,
controlling the rate at which the body burns food, and increasing
alertness when needed. The autonomic nervous system is a part of
the peripheral nervous system and comprises the nerves that
communicate between the brain stem and the body's internal
organs.
[0015] The autonomic nervous system comprises the sympathetic and
parasympathetic branches or systems, and it functions below the
conscious level through complex interactions between its two
branches to respond quickly and continuously to perturbations that
threaten the stability of the body's internal environment.
[0016] Responses to sympathetic and parasympathetic stimulation are
frequently antagonistic. For example, they have opposing or
antagonistic effects on heart rate. Whereas stimulation of the
sympathetic branch increases heart rate, stimulation of the
parasympathetic branch decreases heart rate. In addition, the
body's response to activity in one branch depends on the level of
activity in the other branch.
[0017] A useful, albeit simplistic, analogy for the parasympathetic
and sympathetic branches is that the sympathetic branch functions
as the body's gas pedal and the parasympathetic branch functions as
the body's brakes. Sympathetic and parasympathetic activity make up
a complex, dynamic system that is continuously adjusting to
changing conditions in the body and in the outside environment. The
autonomic nervous system strives to optimize activity in each
branch and to balance the two branches at every passing moment,
depending on both internal and external conditions.
[0018] B. Heart Rate
[0019] Normal rhythmic contractions of the heart occur because of
spontaneous electrical pacemaker activity of cells in the
sinoatrial (SA) node. The heart rate, i.e., the time interval
between heartbeats, is determined by how long it takes the
membranes of these pacemaker cells to spontaneously depolarize to
the threshold level. The heart beats at a spontaneous or intrinsic
rate, which is approximately 100 beats per minute, in the absence
of outside influences. Outside influences are required to increase
or decrease the heart rate from its intrinsic rate.
[0020] The two most important outside influences on heart rate come
from the autonomic nervous system. Fibers from both the sympathetic
branch and parasympathetic branch of the autonomic nervous system
terminate on cells in the SA node and both can modify the intrinsic
heart rate. Activating the cardiac sympathetic nerves increases
cardiac sympathetic tone, thereby increasing heart rate. Increasing
cardiac parasympathetic tone, on the other hand, slows the heart
rate. Both sympathetic and parasympathetic nerves influence heart
rate by altering the course of spontaneous depolarization of the
resting potential in SA pacemaker cells.
[0021] C. Heart Rate Variability (HRV)
[0022] Heart rate variability is the amount of heart rate
fluctuation around a mean heart rate. Such fluctuations reflect
oscillations in sympathetic-parasympathetic balance associated with
a variety of factors, including respiration, baroreceptor reflexes,
vasomotor control, and thermoregulatory processes. The main
periodic fluctuations found are respiratory sinus arrhythmia and
baroreflex-related and thermoregulation-related heart rate
variability.
[0023] Heart rate variability is demonstrated by every normal
person's heart, regardless of that person's state of health and
regardless of the presence of stress or disturbances. Even a
sleeping person displays heart rate variability. Each person has a
measurable baseline heart rate variability even in the absence of
external stressors, such as traffic, screaming babies, and looming
deadlines.
[0024] Heart rate variability can be used as a mirror of the
cardiorespiratory control system, and it is a valuable tool to
investigate the sympathetic and parasympathetic function of the
autonomic nervous system. Heart rate variability provides
information about sympathetic-parasympathetic interplay and
balance, which includes other valuable information about the
nervous system, including, for example, the risk for sudden cardiac
death in patients after myocardial infarction.
[0025] Heart rate variability measurements are easy to perform, are
noninvasive, and are easily and accurately reproducible. In
addition, heart rate variability has been found to be largely
unaffected by placebos.
[0026] Heart rate variability can be influenced by physiologic and
maturational factors. Maturation of the autonomic nervous system
results in an increase in heart rate variability with gestational
age and during early post-natal life. Heart rate variability
decreases with age, and this decline begins in childhood. In
addition, heart rate variability is influenced by provocation and
physical disorders.
SUMMARY OF THE INVENTION
[0027] The present invention relates generally to a method and
apparatus for affecting a subject's health or condition by using
information regarding the sympathetic and/or parasympathetic branch
of the autonomic nervous system to modulate and/or apply stimuli to
the patient. This invention may be used to treat patients with
various pathologies as well as to treat healthy subjects in order
to improve or refine the functioning of their autonomic nervous
system.
[0028] One aspect of the present invention involves stimulation of
the autonomic nervous system based on separate analysis of the
sympathetic branch and/or the parasympathetic branch of the
autonomic nervous system. In a preferred embodiment, this invention
does not require any physical or mental efforts on the part of the
patient, nor does it involve the patient's cognitive awareness or
involvement. The present invention may even be used to treat a
sleeping patient. In a preferred embodiment the autonomic nervous
system is monitored and the stimuli is modulated by continuous
assessment of heart rate variability.
[0029] Another aspect of the present invention is to provide a
real-time feedback loop or system, wherein information about the
autonomic nervous system is continuously monitored and then fed
back or conveyed to the patient through afferent neural pathways,
i.e., nerves that conduct impulses from the periphery of the body
inward to the spinal cord. In a preferred embodiment, this feedback
loop is carried out by application of sensory stimuli.
[0030] Another aspect of the present invention is to coordinate the
stimuli with specific phases of cyclical activities of the body,
such as systole or diastole in the cardiac cycle, and inspiration
or expiration in the breathing cycle.
BRIEF DESCRIPTION OF THE FIGURES
[0031] Reference is next made to a brief description of the
figures, which are intended to illustrate the apparatus and method
according to the present invention. The figures and detailed
description which follow are intended to be merely illustrative,
and are not intended to limit the scope of the invention as set
forth in the appended claims.
[0032] FIG. 1 is a block diagram, generally illustrating an
embodiment of an apparatus of the present invention;
[0033] FIG. 2 is an electrocardiogram (ECG) of a typical series of
heartbeats, each showing the QRS complex, from which a fiducial
point is identified in order to measure heart rate and heart rate
variability;
[0034] FIG. 3 is a schematic of an amplifier, which may be used in
the apparatus of the present invention;
[0035] FIG. 4 is a block diagram, generally illustrating an
embodiment of the method of the present invention;
[0036] FIG. 5 illustrates a typical power spectrum;
[0037] FIG. 6 is a flowchart depicting the function of the signal
processor in one embodiment of the present invention;
[0038] FIG. 7 is a flowchart depicting the function of the stimuli
modulation software in one embodiment of the present invention;
and
[0039] FIG. 8 is a chart summarizing the stimuli parameters in one
embodiment of the present invention as shown in FIG. 4.
DETAILED DESCRIPTION OF THE INVENTION
[0040] The present invention is generally directed toward affecting
a patient's autonomic nervous system, whether to treat a condition
or simply to enhance performance of the autonomic nervous system.
In a preferred embodiment the invention involves applying one or
more default sensory stimuli, such as a visual stimulus and/or an
audio stimulus to the patient, obtaining separate information about
the parasympathetic and/or sympathetic branches of the autonomic
nervous system by monitoring the patient, which information is
preferably used to continuously alter the default stimuli according
to the information obtained. In a preferred method, information
about the autonomic nervous system is obtained by transforming the
patient's ECG electrical signal into a measurement of heart rate
variability parameters as a function of frequency.
[0041] An apparatus according to one embodiment of the present
invention is depicted schematically in FIG. 1. As shown in this
figure, the apparatus generally comprises patient monitor 100,
stimuli modulating unit 200, and stimuli applicator 300.
[0042] Patient or subject monitor 100 is utilized to monitor a
condition of the patient or subject that may be analyzed to
separately assess sympathetic and/or parasympathetic activity of
the patient's autonomic nervous system. The monitored patient
condition or parameter may be any patient activity, including
physiological, cognitive, and behavioral activity of the patient or
subject. In a preferred embodiment, patient monitor 100 comprises
commercially available ECG machine. Other suitable patient monitors
include digital cameras to quantify the amount of the body's
agitation, and a skin resistance galvanometer.
[0043] Signals representative of the patient condition monitored
are transmitted to stimuli modulating unit 200 by patient monitor
100. The apparatus is controlled by stimuli modulating unit 200,
which includes central processing unit (CPU) 210, memory 240, in
which data may be stored, signal processor 250, and stimuli
modulation software 260, which contains the expert system
comprising the algorithm to control the stimulus or stimuli. In a
preferred embodiment, CPU 210 is coupled with monitor 230 and/or
keyboard 220 to provide an interface with the operator of the
apparatus. However, these interface elements are optional and may
comprise alternative elements known in the art. Several of these
components (i.e., CPU 210, keyboard 220, monitor 230, memory 240,
signal processor 250) are well known in the art. In fact, these
components are included in most existing computers.
[0044] In an exemplary embodiment, the apparatus includes an
electrocardiograph connected to a standard Intel based personal
computer (PC) or workstation with a color monitor, a soundboard,
and an audio headset. The electrocardiograph is used to measure
electrical currents associated with heart muscle activity, from
which the subject's HRV may be analyzed and the subject's
sympathetic and/or parasympathetic activity assessed. The algorithm
is executed by the PC, the visual output is displayed on the color
monitor, and the audio output is transmitted by the headset.
[0045] The functions performed by signal processor 250 are shown in
the flowchart of FIG. 6. First, as indicated in step 510, signal
processor 250 amplifies the signal received from patient monitor
100. Then, in step 520, the analog signal is converted to a digital
signal. It should be noted that some of the steps shown in FIG. 6
may not be required. For example, if the signals received by signal
processor 250 are digital signals, then converting the signal to a
digital signal, i.e., step 520, is obviously not necessary. Next,
in step 530, a time domain analysis is performed on the signal.
This is followed by step 540 comprising a frequency domain analysis
of the signal. The hardware necessary to perform these functions
are well known in the art. Alternatively the functionality of
signal processor 250 may be incorporated in software as part of
stimuli modulation software 260 or separately in memory 240, as is
known in the art. Ultimately, signal processor 250 produces output
parameters which are indicative of sympathetic and/or
parasympathetic activities of patient 400.
[0046] The stimulation modulation software 260 then determines the
appropriate stimuli or the appropriate modulation of the stimuli.
The algorithm by which this is accomplished is depicted in the
flowchart of FIG. 7, and is described in detail herein.
[0047] Stimuli applicator 300 applies appropriate stimulus to the
patient 400 based on information received from modulating unit 200.
As schematically represented in FIG. 1, applicator 300 may include
headphones for sound stimulus and a television or computer monitor
for light stimulus. Light stimuli may be applied in any suitable
manner, such as by LCD or CRT monitor, incandescent, fluorescent,
or neon lighting. A variety of different types of stimuli also may
be used, including pressure applied to the patient and thermal
radiation. Also, virtual reality helmets may be used as applicator
300, including an open or closed helmet with glasses and earphones
attached. The apparatus may be completely wireless, incorporating
infrared technology.
[0048] According to further alternative embodiments of the present
invention, it is not necessary that all components of the apparatus
be located together or directly linked together. For example, a
subject provided with ECG electrodes and an appropriate interface
to his or her own home computer could access modulating unit 200 or
both patient monitor 100 and modulating unit 200 at a remote
location via the Internet or a direct dial-in connection. The
multimedia features of current home computers make them ideal
stimuli applicators 300, providing both sound and light stimuli.
Alternatively, patient monitor 100 and stimuli applicator 300 may
be incorporated together into a home unit which would communicate
with modulating unit 200 via a standard telephone connection. In
this embodiment, subjects without home computers or computer
skills. would simply dial in to the care provider for treatment
without having to make regular visits to a clinic or other
treatment center. Regular, periodic dial-in sessions would create
individual patient histories which may be automatically monitored
for significant variations to provide automatic warnings to
appropriate caregivers as may be required. In alternative
embodiments, signal processor 250 and stimuli modulation software
260 may be downloaded from the Internet to home computers, such as
personal computers, Web TVs, and Digital TVs.
[0049] According to the method of the present invention, the
patient is monitored in order to ascertain the activity of the
sympathetic and/or parasympathetic branch of the patient's
autonomic nervous system. In the preferred embodiment, this
activity is assessed through analysis of the patient's heart rate
variability (HRV). Measuring heart rate variability, as opposed to
simply heart rate, yields a wealth of information about the
autonomic nervous system which leads to improved diagnosis of a
variety of pathologies, including hypertension, cardiac ischemia,
myocardial infarction, diabetic neuropathy and other autonomic
dysfunctions. Improved diagnosis in turn leads to more specific,
and subsequently more effective, therapy.
[0050] The measurement of heart rate variability contains
information about several physical parameters of autonomic nervous
system activity, including heart rate. Therefore, this measurement
provides a multi-dimensional picture of the patient's condition
that could not otherwise be obtained from simply measuring a
patient's heart rate, which is simply the average number of
heartbeats in a certain time interval. Information about the
autonomic nervous system provided by heart rate variability as
compared to heart rate is analogous to information about a moving
body provided by acceleration and velocity measurements as compared
to only velocity measurements. Because heart rate variability
measurements provide a more complete picture, better diagnosis and
treatment may be obtained based on analysis of heart rate
variability as opposed to simply heart rate.
[0051] It should be noted, however, that any method of monitoring
or assessing the activity of the autonomic nervous system, for
example breathing, may be incorporated in the present invention as
long as sympathetic and/or parasympathetic branch of the autonomic
nervous system may be separately analyzed.
[0052] As shown in FIG. 7, in step 610, the signal processor output
parameters are mathematically manipulated. According to the
preferred method, autonomic nervous system activity is measured
through statistical and spectral analysis of a series of RR
intervals from a patient's ECG. In this analysis, a resting
baseline ECG of the patient is first obtained by a standard
electrocardiograph. Such an ECG is illustrated in FIG. 2. As shown
in FIG. 2, each heartbeat includes what is referred to as a QRS
complex. The average QRS complex spans approximately 75 msec.
[0053] Errors, resulting from noise effects, missing data,
arrhythmic or ectopic beats will distort the HRV analysis. These
errors may be eliminated or reduced through interpolation of
previous or successive RR intervals in the ECG signal. Other
algorithms known in the art such as linear regression or
auto-correlation functions may also be used to reduce these
errors.
[0054] To obtain an HRV measurement with minimal noise, the subject
is instructed not to engage in certain activities for approximately
four hours prior to taking the ECG, which may affect his heart rate
and heart rate variability, such as taking stimulants, including
chocolate. The ECG is taken while the subject is at rest in a
semi-supine position. While the heart rate is being monitored, the
subject is instructed to keep still and to avoid physical and
mental activities such as chewing gum making mathematical
calculations, or memorizing information.
[0055] The ECG signal, which is in the millivolt range, is then
amplified into the volt range (see step 510 in FIG. 6). Several
amplification methods are known in the art. Typically, amplifiers
are manufactured to order, and special hardware may be designed for
this particular application. A type of ECG amplifier schematic that
is appropriate for the present invention is shown in FIG. 3. This
circuit diagram employs known symbols for circuit elements. This
ECG amplifier has two input bootstrapped buffers as an input stage
and a differential amplifier as an output stage. The bootstrapped
buffer makes the input impedance very high both at d.c. and at 50
Hz. The total gain for this amplifier is 180.
[0056] One of ordinary skill will be able to construct an amplifier
based on this circuit diagram or an appropriate amplifier for the
present invention. Alternatively, one may incorporate already
existing software or any other suitable amplification method known
in the art.
[0057] The analog signal is then sampled and transformed into a
digital signal by a standard digital signal processing card
preferably having 12-bit resolution (see step 520 in FIG. 6). The
electrocardiograph itself may be provided with a digital output.
For the present method, it is sufficient to sample the ECG signal
at a rate of 1000 times per second. Obviously, however, sampling at
a higher rate up until the Nyquist rate will improve the accuracy
of the digital representation, whereas sampling at a lower rate
reduces the signal to noise ratio. In any case, a sampling rate
lower than 500 times per second is not recommended.
[0058] The digital signal is then processed using the pattern
recognition method described herein, or any other suitable
mathematical technique known in the art (see step 530 in FIG. 6).
The pattern recognition method is preferred because it renders
accurate readings even for erratic signal patterns, such as may be
obtained, for example, from hyperactive kids. Through more
sophisticated pattern recognition methods, increasingly stable and
noise independent fiducial points may be obtained based on fewer
data points.
[0059] According to standard pattern recognition methods known in
the art, linear regression is used on the QR slope data and the RS
slope data to obtain the most representative straight line for both
the QR slope and the RS slope. The point of intersection of both
lines represent the R.sub.n fiducial point for each QRS waveform n.
The time lag between R.sub.n and R.sub.n+1 is the value RR, which
is typically expressed in milliseconds, and a series of RR values
is referred to as an RR series.
[0060] The RR values resulting from sinoatrial (SA) node
depolarization are referred to as normal to normal (NN) intervals,
and in the present invention, only the NN values are considered.
The method of transforming an RR series into an NN series is well
known in the art.
[0061] A series of NN values are measured over a segment or time
window defined by N heartbeats, and the heart rate variability
parameters of interest are calculated based on the ECG recording
within the segment, where N=30k and k is an integer between 1 and
6, and 4 and 120 are the default values for k and N respectively.
This process is repeated continuously until the end of the
treatment over successive segments or time windows, wherein the
starting points of each time window are separated by a
predetermined interval. In the preferred embodiment, this
predetermined interval is determined by M heartbeats. The preferred
values for M are 4, 10, and 23, and 4 is the default value for
M.
[0062] Typically, the subject's ECG is obtained both to measure the
subject's baseline HRV and to monitor the subject during treatment.
The type of stimuli to be used and the parameters of the stimuli to
be modulated is initially determined based on evaluation of the
patient's baseline HRV. This baseline HRV may be obtained from a
single ECG segment of at least 1500 RR intervals, which typically
will require no more than 24 minutes of recording. During
treatment, on the other hand, the subject's ECG is continuously
obtained until the end of the treatment, wherein several successive
segments of heartbeats are identified, and wherein each segment
comprises N heartbeats, as explained above. Stimuli is applied and
modulated based on information about the autonomic nervous system
obtained from these segments.
[0063] Heart rate variability may be determined in at least the
following two ways: (1) by calculation of indices based on
statistical operations on NN intervals (time domain analysis) or
(2) by spectral (frequency domain) analysis of an array of NN
intervals.
[0064] To assess baseline values of HRV or to calculate changes in
ANS activity during treatment, the following time domain parameters
of HRV may be used in the present method:
[0065] (1) mean NN intervals (mNN);
[0066] (2) mean heart rate as derived from the mean NN
intervals;
[0067] (3) difference between the minimal and maximal NN
interval;
[0068] (4) standard deviations of the NN intervals (SDNN), which is
the root of variance;
[0069] (5) root mean square of successive differences in the NN
intervals (rMSSD); and
[0070] (6) percentile of intervals in which the absolute value of
the difference between them is greater than 50 msec (Pnn50).
[0071] In alternative embodiments of the present invention, these
parameters and other time domain parameters of ANS activity can
provide information to apply or modulate sensory stimuli to the
patient.
[0072] Frequency domain analysis or power spectral density (PSD)
analysis is a preferred method for measuring heart rate variability
according to the present invention (see step 540 in FIG. 6). This
analysis provides a measurement of power as a function of
frequency. PSD analysis enables one to evaluate the contribution to
variance of frequency-specific oscillations. Therefore, one can
measure not only the amount of variability, but also its
distribution in relation to oscillation frequency (i.e., number of
heart rate or NN value fluctuations per second). The point of PSD
analysis is to pass from a continuous time function, i.e., the ECG
tracing or curve, to a discrete-time function, which is
representative of the instantaneous heart rate.
[0073] There are a number of methods of performing frequency domain
analysis. Two popular methods include fast Fourier transformation
and autoregressive (AR) modeling. Both methods are well known in
the art, and both yield similar results. Fast Fourier
transformation spectra, however, are characterized by discrete
peaks for the several frequency components, whereas the
autoregressive method results in continuous smooth spectra of
activity. Fast Fourier transformation analysis is particularly
useful in determining the area under the spectrum, whereas AR
modeling is particularly useful in determining central tendencies
or peaks in given frequency ranges.
[0074] Other methods of performing frequency domain analysis
includes coarse-graining spectral analysis, useful in extracting
the harmonic components from a broad band noise spectrum and
wavelet analysis, capable of processing data at different scales or
resolutions, a method well suited for approximating data containing
sharp discontinuities.
[0075] Fourier analysis is a simple, widely used technique that
involves decomposing the series of sequential RR intervals into a
sum of sinusoidal functions of different amplitudes and
frequencies. The resulting power spectrum can be graphed as the
magnitude of variability as a function of frequency. The power
spectrum therefore reflects the amplitude of the heart rate
fluctuations present at different oscillation frequencies. Fourier
analysis may be performed on a short ECG recordings of 0.5 minute
to several minutes to much longer recordings.
[0076] The autoregressive model is a linear prediction formula that
attempts to predict an output y[n] of a system based on previous
outputs (y[n-1], y[n-2], etc.) and inputs (x[n], x[n-1], x[n-2],
etc.). Deriving the linear prediction formula involves determining
the coefficients a1, a2, and b0, b1, b2, etc. in the following
equation:
y[n](estimated)=a1*y[n-1]+a2*y[n-2] . . . +b0*x[n]+b1*x[n-1]+. .
.
[0077] In other words, the system is determined by calculating a
set of coefficients that yield an accurate prediction y[n]. The
model will differ depending on the order of the model (n) chosen,
higher orders corresponding to larger n values. Higher order models
will produce higher resolution and more noise, whereas lower order
models will produce less noise, or a smoother result, comprising
less information.
[0078] Other than the time and frequency analyses of a series of NN
intervals outlined above, geometrical methods can also provide
information about HRV. The most common geometrical methods include:
a) sample density distribution of NN interval duration; b) sample
density distribution of differences between adjacent NN intervals;
and c) Lorenz plot of NN or RR intervals (i.e., XY scattergram).
These analyses yield shapes or patterns (e.g., the Lorenz plot
characteristically yield linear, triangular, or elliptical shapes),
and the geometrical and/or graphical properties of these resulting
patterns provide information about various aspects of HRV. Again,
any method that provides separate information about the sympathetic
and/or parasympathetic branches of the ANS may be incorporated in
the present invention.
[0079] Periodic variations in heart rate at different frequency
ranges reflect different aspects of autonomic nervous system
activity. The method of the present invention generally focuses on
short-term ECG segments. For these short term segments, the
following spectral bands may be identified: a) a very low frequency
(VLF) band from 0.003 Hz to 0.04 Hz; b) a low frequency (LF) band
from 0.04 Hz to 0.15 Hz; and c) a high frequency (HF) band from
0.15 Hz to 0.45 Hz in adults and to 0.5 Hz in children. Because the
VLF band may contain many non-periodic components and may be
significantly affected by certain algorithms used in the HRV
analysis, it is not considered in a preferred embodiment of the
present invention.
[0080] In a preferred embodiment of the present invention, PSD
analysis is used to measure heart rate variability. Specifically,
the 16th order transformation of the AR model is used. An example
of a power spectrum obtained by such an analysis is shown in FIG.
5, depicting a graph of variability (i.e., power) as a function of
frequency (Hz).
[0081] Variability measurements below 0.15 Hz on the variability
curve is generally a reflection of sympathetic activity, and
variability measurements above 0.15 Hz is generally a reflection of
parasympathetic activity. Separate evaluation of the
parasympathetic and sympathetic branches of the ANS is possible
through PSD analysis. The power spectrum may also be divided into
several other frequency bands or regions, in addition to or other
than these two classical frequency regions. For example, the power
spectrum above 0.04 Hz may be divided into three regions, wherein
the lower region is below approximately 0.08 Hz, the middle region
is located between approximately 0.08 Hz and 0.15 Hz, and the high
region is above 0.15 Hz.
[0082] Characterizing each frequency region are two parameters of
particular interest: the area under the curve of the power spectrum
(S), which represents variance and is measured in msec.sup.2; and
the height of the curve at its peak (A), representing the maximum
value of variability within the frequency region and is measured in
msec.sup.2/Hz. The methods of the present invention also includes
other parameters which are derived from these parameters,
including: (a) absolute value in the change in S over consecutive
time windows (.vertline..DELTA.S.vertline.); (b) the absolute value
in the change in A over consecutive time windows
(.vertline..DELTA.A.vertline.); (c) the absolute fractional change
of S (.vertline..DELTA.S.vertline./S); and (d) the absolute
fractional change of A (.vertline..DELTA.A.vertline./A).
[0083] According to the present invention, the stimuli applied to
the patient or subject may comprise any stimuli, including the
sensory stimuli of taste, smell, and touch. In the preferred method
of the present method, a background stimuli is first applied to the
patient. The background stimuli includes a visual background
stimulus and/or an audio background stimulus. Then, as shown in
FIG. 7, the stimuli is modulated according to the activity of the
sympathetic and/or parasympathetic branches of the subject's
autonomic nervous system (see step 620).
[0084] The background stimuli may have default values for certain
parameters, depending on the type of stimuli. For light and sound
stimuli, frequency default values may be given. Background values
for color brightness and sound loudness will preferably be adjusted
to a minimum, and according to each individual subject, and his
level of perception.
[0085] In the preferred embodiment, the visible color spectrum
frequency range is taken to be approximately 380 nm to
approximately 770 nm. This visible frequency range is divided into
a low frequency color spectrum of approximately 380 nm (red) to
approximately 530 nm (green) and a high frequency color spectrum of
approximately 530 nm to approximately 770 nm (blue). The visual
background stimulus generally consists of one of two colors. One
default color has a hue within the high frequency color spectrum
(i.e., approximately 490 nm), and the other default color has a hue
within the low frequency color spectrum (i.e., approximately 600
nm).
[0086] In the preferred embodiment, the audible frequency range is
taken to be approximately 25 Hz to approximately 1320 Hz. This
audible frequency range is divided into a low frequency range from
approximately 25 Hz to approximately 440 Hz, and a high frequency
range from approximately 440 Hz to approximately 1320 Hz. One
default sound has a frequency of approximately 880 Hz, which falls
within the high frequency range, and the other default sound has a
frequency of approximately 234 Hz, which falls within the low
frequency range. In addition, the audio background stimulus
comprises any type of sound, such as pink noise or violin sounds
and has a given loudness.
[0087] In alternative embodiments, the background values for the
sound stimulus is total silence (i.e., frequencies below 16 Hz) and
total darkness.
[0088] In the present method, the stimuli is applied during a
predefined time interval, which lasts at least 60 msec, referred to
as an "action time" (T.sub.A). When the stimuli consists of light,
T.sub.A should not be shorter than 60 msec to avoid fundamental
frequencies above 16 Hz because these may trigger epileptic
seizures. Other types of stimuli may have similar limitations.
[0089] The action time T.sub.A repeats itself after an interval
designated as time off or T.sub.OFF. during which the stimulus will
return to its background values. In one embodiment, the stimuli
modulation follows a cycle time equal to T.sub.A+T.sub.OFF. These
time values are previously defined for each type of stimulus at the
expert system in the stimuli modulation software 260.
[0090] When the stimuli is to be applied in synchronization with a
biological cycle, there will be a time interval, referred to as
"waiting time" (T.sub.W), between a fiducial point of the
biological cycle and the beginning of T.sub.A. For example, when
the stimuli is to be applied in synchronization with the cardiac
cycle, T.sub.W is the interval between the R.sub.N fiducial point
and the beginning of T.sub.A. T.sub.A may end before the occurrence
of the next R.sub.N+1 fiducial point, resulting in an additional
"gap time" (T.sub.G) between the end of an action time and the
beginning of the next waiting time, starting at the next fiducial
point. For example, if an NN interval is 783 msec, with a T.sub.W
of a 200 ms; and T.sub.A of 180 msec, then
T.sub.G=T.sub.NN-(T.sub.W+T.sub.A)=- 403 msec.
[0091] It should be noted that, when a single stimulus is applied
and modulated in synchronization with a biological cycle, the total
cycle time for that particular stimulus will be equal to
T.sub.W+T.sub.A+T.sub.G, where T.sub.W and T.sub.A have been
previously defined, for each type of stimulus and/or its parameters
within stimuli modulation software 260.
[0092] During each action time T.sub.A, the stimuli are altered
away from their default values (see step 620 in FIG. 7), according
to two parameters referred to as the Stimulus Control Index (SCI)
and the Directional Index (D). The Stimulus Control Index (SCI)
determines the degree of change from the default for any stimulus
parameter, and the Direction Index determines the direction of
change from the default. In this example, there are four SCIs,
including an SCI for color hue (.theta.hu), an SCI for color
brightness (.theta.br), an SCI for sound volume (.theta.lo), and an
SCI for sound frequency (.theta.sf). The SCI is either the absolute
fractional change of S or the absolute fractional change of A, and
will be a number between 0 and 1. The Directional Index is either
D+ or D-, depending on the increase or decrease of S or A. D(s) is
D+ if S increases, D(s) is D- if S decreases, D(a) is D+ if A
increases, and D(a) is D- if A decreases. These values are
explained in more detail in the following example, which is
explained with reference to FIG. 8.
[0093] In this example, the values for S,
.vertline..DELTA.S.vertline., A, .vertline..DELTA.A.vertline. are
first calculated. As summarized in FIG. 8, the absolute fractional
change of S is calculated, and this value determines the degree of
the change from the default value of color hue and sound loudness.
In other words, in this example, the SCI for color hue (.theta.hu)
and sound loudness (.theta.lo) is .vertline..DELTA.S.vert-
line./S.
[0094] The direction of this change is determined by the
Directional Index for S. The Directional Index is D(s)+ if S
increases from one time window to the next, and the Directional
Index is D(s)- if S decreases from one time window to the next. If
the light stimulus default value is between green and blue, D(s)+
indicates a change in color toward the blue region of the color
spectrum, and D(s)- indicates a change in color toward the green
region of the color spectrum. In addition, D(s)+ indicates an
louder audio signal, and D(s)- indicates a quieter audio
signal.
[0095] The absolute fractional change of A is also calculated, and,
as summarized in FIG. 8, this value determines the value for the
change from the default value of color brightness and sound
frequency. In other words, in this example, the SCI for color
brightness (.theta.br) and sound frequency (.theta.sf) is
.vertline..DELTA.A.vertline./A.
[0096] The direction of this change is determined by the
Directional Index for A.
[0097] The Directional Index is D(a)+ if A increases from one time
window to the next, and the Directional Index is D(a)- if A
decreases from one time window to the next. D(a)+ indicates an
increase in brightness, and D(a)- indicates a decrease in
brightness. In addition, D(a)+ indicates an increase in sound
frequency, and D(a)- indicates a decrease in sound frequency.
[0098] Depending on these parameters, the stimuli shifts away from
the default during T.sub.A. Specifically, as set forth above, one
default audio stimuli is a sound having a frequency of 234 Hz. The
SCI for sound frequency, or .theta.sf, is used to determine the
change in frequency required. This is determined by multiplying
.theta.sf with the appropriate sound frequency scale range. The
appropriate sound frequency scale range represents the maximum
possible range in which the sound frequency may shift.
[0099] In this example, the sound frequency is limited to the low
frequency range of 25 Hz to 440 Hz. Therefore, the sound frequency
may shift from 234 Hz up to a maximum of 440 Hz (a scale range of
206 Hz) or may shift down from 234 Hz to a minimum of 25 Hz (a
scale range of 209 Hz). Given .theta.sf=0.5, therefore, two results
may occur. If D(a)=D+ (due to an increase in A from the previous
time window), the change in frequency would be a positive shift of
0.5 (206 Hz) or 103 Hz. In other words, the sound frequency will
shift up by 103 Hz to a sound frequency of 337 Hz, which is applied
during T.sub.A. If, on the other hand, D(a)=D- (due to a decrease
in A from the previous time window), the change in frequency would
be a negative shift of 0.5 (209 Hz) or approximately 104 Hz. In
other words, the sound frequency will shift down by 104 Hz to a
sound frequency of 130 Hz, which is applied during T.sub.A.
[0100] In this example, as shown in FIG. 8, the SCI for color hue
(.theta.hu) and sound loudness (.theta.lo) are determined by the
absolute fractional change in S, and the SCI for color brightness
(.theta.br) and sound frequency (.theta.sf) are determined by the
absolute fractional change in A. However, in alternative
embodiments, the four SCIs may also be determined by any
combination of the absolute fractional changes in S and A.
Similarly, the Directional Index for any of the stimuli parameters
may also be determined by any combination of changes in S and A.
Appropriate scale ranges must be determined for each stimuli
parameter to be modulated, and these scale ranges are stored within
stimuli modulation software 260.
[0101] In alternative embodiments of the present invention, stimuli
modulation is determined by further multiplying the SCI with a
feedback index value (FIV) between 0 and 1 (0<FIV<1)
depending on the effect intended. For example, if a positive
feedback effect is intended, the stimuli modulation will be
determined by SCI*FIV (e.g., FIV=0.8) for each D- that appears. In
other words, stimuli shifts in the negative direction will be less
than stimuli shifts in the positive direction. Similarly, if a
negative feedback effect is intended, the stimuli modulation will
be determined by SCI*FIV for each D+ that appears. An FIV of 1
indicates equal shifts in both directions.
[0102] FIV values will depend on the effect desired on sympathetic
or parasympathetic activity. Initial assessment of a patient, based
on baseline HRV measurement, together with accumulated experience
and empirical data, will assist in determining FIV values at the
expert system in stimuli modulation software 260 of the apparatus.
Other alternative means of introducing a negative or positive
feedback component in the applied and/or modulated stimuli may be
incorporated in the present invention.
[0103] In an alternative method of the present invention, the
stimuli is applied in coordination with cyclical activity of the
body, such as with systole or diastole of the cardiac cycle and/or
with inspiration or expiration of the breathing cycle.
[0104] It has been found that the balance between sympathetic and
parasympathetic activity shifts or changes during periodic
biological cycles, such as the menstrual and breathing cycles.
There is also a circadian rhythm to ANS activity, wherein
parasympathetic activity increases during the night and sympathetic
activity increases during the day. In short biological cycles, like
the heartbeat, a rise in sympathetic tone increases myocardial
contractility and may prolong systolic ejection time. In addition,
the QT interval in the electrocardiogram corresponding to the
depolarization-repolarization cycle of the ventricles also
increases if sympathetic activity increases. Sympathetic activity
is related to the duration of systole and inspiration, and the
diurnal period. Similarly, parasympathetic activity is related to
the duration of diastole and expiration, and the nocturnal period.
Due to these relationships, an alternative method of modulating the
stimuli comprises establishing a veto for predefined sections or
phases of a selected biological cycle. Halting or withdrawing the
stimulus during predefined phases of a biological cycle optimizes
the chances of selectively influencing the sympathetic and/or the
parasympathetic branches of the ANS.
[0105] Stimuli may therefore be applied in coordination with
systole to stimulate the sympathetic branch, while stimuli may be
applied in coordination with diastole to stimulate the
parasympathetic branch.
[0106] This synchronization is obtained by the following
algorithm:
[0107] 1) The mean value of a normal cardiac cycle (mNN) is
obtained in milliseconds (msec), from 30 prior successive NN
intervals.
[0108] 2) The time period between the fiducial point, calculated by
the method shown above, and the end of the mechanical systole, is
the systolic interval or T.sub.S. This systolic interval can be
calculated to a precision of a few milliseconds by the following
equation:
T.sub.s=512-(123.000/mNN) (1)
[0109] 3) To coordinate T.sub.A with T.sub.S, the following
equations are used:
T.sub.W+T.sub.A=T.sub.S-Z (2)
T.sub.A.gtoreq.60 (3) (for light stimuli)
[0110] where T.sub.W is the waiting time in msec, between the
fiducial point and the 10 beginning of T.sub.A. Any combinations
for T.sub.W and T.sub.A values are possible.
[0111] 4) To coordinate T.sub.A with the cardiac interval following
T.sub.S, known as diastole, the following equations are used:
T.sub.W=T.sub.S+Z (4)
T.sub.A.ltoreq.mNN-(T.sub.W+180) (5)
T.sub.A.ltoreq.60 (6) (for light stimuli)
[0112] The value of Z, given in msec, may differ for different
stimuli acting on the same biological cycle. In the preferred
method, the value of Z is at least 40 msec.
[0113] Alternatively, stimuli may be applied in coordination with
inspiration to stimulate the sympathetic branch, while stimuli is
applied in coordination with expiration to stimulate the
parasympathetic branch.
[0114] Stimuli coordination with the breathing cycle may require
monitoring the lung movements in order to determine the beginning
and the end of the inspiration period, as well as the total
duration of the respiratory cycle. On average, the breathing cycle
T.sub.B, is four times longer than the cardiac cycle and generally
lasts more than 3000 msec. The inspiration period generally lasts
around 1000 msec. Therefore, even if the breathing monitoring
device may not be highly accurate, synchronizing stimuli
application and/or modulation lasting around 200 or 300 msc, for
example, with relatively long inspiration or expiration periods,
will not present technical difficulties. Devices for monitoring the
breathing cycle include chest belts with, for example,
piezo-electric sensors. Another type of monitor includes finger
photopletismographic devices which are sensitive to venous pressure
oscillations coupled with the mechanical wave generated by the
lung's periodical volume changes during the breathing cycle. After
eliminating noise by proper filtering, the first derivative of the
breathing wave form curve will identify the beginning and end of
the inspiration slope. Once the respiratory fiducial point at the
beginning of the inspiration phase is obtained, average values for
the breathing cycle (mT.sub.B) together with its two main phases,
can then be calculated. Stimuli coordination with breathing can be
achieved with the following algorithm, with values given in msec.
To coordinate T.sub.A with the inspiration period T.sub.1 the
following equations are used:
T.sub.W+T.sub.A=T.sub.1-Z (7)
T.sub.A.gtoreq.60 (8) (for light stimuli)
[0115] To coordinate T.sub.A with the expiration period the
following equations are used:
T.sub.W=T.sub.1+Z (9)
T.sub.A.ltoreq.mTB-(TW+Z) (10)
T.sub.A.ltoreq.60 (11) (for light stimuli)
[0116] where T.sub.W is the time interval between the respiratory
fiducial point and the beginning of the action time T.sub.A., in
msec and mT.sub.B is the average T.sub.B value of at least 5
breathing cycles, in msec. The value of Z may change according to
the case and in the preferred method Z is at least 100 msec.
[0117] Simultaneous coordination with the cardiac and respiratory
cycle is also possible. In order to influence sympathetic activity
equations (1), (2), and (7) are used. T.sub.A will be given the
same value in the above algorithms. These particular combinations
could be proven to be very useful in patients suffering from
asthma. Similarly, in order to influence the parasympathetic
activity equations (1), (4), (5), (9), and (10) are used, giving
T.sub.A the same value.
[0118] One aspect of the invention relates to the timing of
application of the stimuli. In a further alternative method of the
present invention, for example, the stimuli may be applied at
certain times that are significant to sympathetic or
parasympathetic activities. As stated above, it is known that the
sympathetic branch of the autonomic nervous system is more active
during the day, and that the parasympathetic branch of the
autonomic nervous system is more active at night. Therefore, one
aspect of the invention is to apply sensory stimuli either during
the day or during the night depending on which branch of the
autonomic nervous system to be stimulated or the particular health
situation to be addressed.
[0119] It should also be noted that the time of day also has an
effect on HRV measurement. HRV measurements for clinical evaluation
may require short time ECG recordings, including at least nine
hundred RR intervals. Although these recordings may be taken at any
time, they should be renormalized for a predefined hour, such as 12
am. To compare HRV changes in one patient before and after
treatment, more accurate results will be obtained if the ECG
recordings of that patient are taken having the same length and at
the same hour in the day.
[0120] The following examples are intended to illustrate the method
according to the present invention. However, they are not intended
to limit the scope of the invention as set forth in the appended
claims.
EXAMPLE 1
[0121] In this example, a subject is treated for insomnia and/or
stress by stimulating or increasing parasympathetic activity of the
autonomic nervous system. It should be noted that an evaluation of
the patient's baseline HRV may indicate that rather than increasing
parasympathetic activity, sympathetic activity should be decreased.
Alternatively, the patient's condition may require stimulation of
both parasympathetic and sympathetic activity, but wherein
parasympathetic activity should be increased more than sympathetic
activity.
[0122] As described above, the patient's ECG is first recorded
before the beginning of the treatment to make a preliminary
diagnosis of the patient and to determine generally what method of
treatment is indicated. During treatment, the patient's ECG is
continuously obtained from which successive power spectrum (PS)
variability curves are continuously derived from successive and
overlapping time windows, wherein each time window spans 120
heartbeats (i.e., k=4 (default value) and N=30k=120 heartbeats) or
approximately 2 minutes of the ECG recording. The beginning of each
window is separated from beginning of the previous window by M=10
heartbeats or approximately 10-second intervals.
[0123] In this example, the region of interest is the frequency
range over 0.15 Hz, which, as stated above, is thought generally to
be a reflection of parasympathetic activity. The stimuli is
determined based on changes in the area below the power spectrum
curve in this region. An increase in area reflects an increase in
parasympathetic activity, and a decrease in area reflects a
decrease in parasympathetic activity. In one embodiment of the
present invention, feedback of this information comprises
modulation of light and/or sound stimuli which follows a positive
feedback correlation with the measured changes in the area under
the curve and in the height of the highest peak in this region.
[0124] Because a parasympathetic effect is intended, the visual
stimuli will comprise colors in the range between green and violet.
The audio stimuli will comprise sounds in the low frequency range
between approximately 25 and approximately 440 Hz.
[0125] The background default visual stimulus is a blue-green color
of 490 nm. This color is displayed on a TV or computer screen. The
area S under the variability curve in the region of interest is
continuously monitored. If this area increases, the color of at
least a part of the screen is shifted toward the violet end of the
color spectrum during T.sub.A, and if this area decreases, the
color is shifted toward the green end of the color spectrum during
T.sub.A. The SCI for color hue (.theta.hu) is
.vertline..DELTA.S.vertline./S. Because a positive feedback effect
is desired, color shifts toward the green end of the spectrum will
be smaller than color shifts toward the violet end. In this
example, the FIV=0.8, and for every D-, the stimuli shift is
determined by the product of FIV and SCI.
[0126] The central tendency peak or the height of the curve at its
peak A in the region of interest is also continuously monitored,
and as the height of the peak increases, the brightness of the
color is increased, and as the height of the peak decreases, the
brightness is decreased, albeit in smaller proportion than the
increase in intensity. The SCI for color brightness (.theta.br) is
.vertline..DELTA.A.vertline./A. Again, for every D-, the stimuli
shift is determined by the product of FIV and SCI.
[0127] The background auditory stimulus is a sound having a
frequency of 234 Hz, which is within the low frequency range
between 25 Hz and 440 Hz. As the area S under the variability curve
in the region of interest increases, the frequency of the sound
during T.sub.A is decreased, and as the area S decreases, the
frequency of the sound is increased, albeit in smaller proportion
than the decreases in pitch. In addition, as the height of the
central tendency peak above 0.15 Hz increases, the loudness of the
sound is decreased, and as the height of the central tendency peak
above 0.15 Hz decreases, the loudness of the sound is increased,
albeit in smaller proportion than the decreases in loudness.
Modulation of loudness is determined in the same way as the
modulation of brightness, which is described above.
[0128] In addition, the stimuli duration T.sub.A may be constant or
may vary from approximately 0.06 seconds to approximately 0.18
seconds. The default duration is approximately 0.12 sec. As
parasympathetic activity increases, T.sub.A is increased, and as
parasympathetic activity decreases, T.sub.A is decreased, albeit in
smaller proportion than the increases in duration. In other words,
in this example there is an SCI for T.sub.A which is equal to
.vertline..DELTA.S.vertline./S. T(off), which represents the time
period between consecutive auditory stimuli, may be determined in
at least one of the following two ways.
[0129] In one embodiment of the method, T.sub.A is not synchronized
with any biological cycle, and T(off) may be any arbitrary
predefined time value.
[0130] In an alternative embodiment, the stimuli is applied during
a predefined phase of a physiological cycle. As explained
previously for example, the stimuli may be synchronized with the
cardiac cycle, using the R fiducial point of the QRS complex as a
reference. To increase parasympathetic activity, the stimuli will
be applied during the latter or diastolic phase of the cardiac
cycle. The delay (T.sub.W) between the fiducial point in the QRS
complex of the ECG signal and the starting point of the stimuli is
calculated according to equations (1), (4), (5), and (6) explained
above.
EXAMPLE 2
[0131] In this example, a subject is treated for migraines and/or
poor mental concentration by assisting the autonomic nervous system
in stimulating or increasing sympathetic activity. Again,
alternative treatments may be possible. The subject's baseline
heart rate variability is initially determined by time domain and
spectral analysis as described above.
[0132] From the patient's ECG, successive PS variability curves are
obtained from successive and overlapping time windows, wherein each
time window spans 90 heartbeats (i.e., k=3 and N=30k=90 heartbeats)
or approximately 1.5 minutes of the ECG recording. The beginning of
each window is separated from the beginning of the previous one by
M=4 heartbeats, which is approximately 4 seconds. In this example,
the power spectrum region of interest is below 0.15 Hz, and the
stimuli is determined based on changes in the area and in the
height of the highest peak in this region.
[0133] Because a sympathetic effect is intended, the visual
stimulus will comprise colors ranging from green to red, and the
audio stimulus will range from approximately 440 Hz to
approximately 1320 Hz.
[0134] The background visual stimulus is an orange color of
approximately 600 nm, which is a color between green and red. This
color is displayed on a TV or computer screen. The area S under the
variability curve in the region of interest is continuously
monitored. If this area increases, the color of at least part of
the screen is shifted toward the red end of the color spectrum, and
if this area decreases, the color of at least part of the screen is
shifted toward the green end of the color spectrum. Color shifts
toward the green end of the spectrum are smaller than color shifts
toward the red end. In this example, the SCI for color hue
(.theta.hu) is .vertline..DELTA.S.vertline./S, and FIV=0.7.
[0135] The height of the central tendency peak A in the region of
interest is also continuously monitored, and as the height of the
peak increases, the brightness or intensity of the color is
increased, and as the height of the peak decreases, the intensity
is decreased, albeit in smaller proportion than the increase in
intensity. In this example, SCI for color brightness (.theta.br) is
.vertline..DELTA.A.vertline./A.
[0136] The background auditory stimulus is a sound having a
frequency of 880 Hz. As sympathetic activity increases, the
frequency of the sound is increased, and as sympathetic activity
decreases, the frequency of the sound decreases, albeit in smaller
proportion than the increases in pitch. The SCI value for sound
frequency (.theta.sf) is .vertline..DELTA.S.vertline./S.
[0137] In addition, as the height of the central tendency peak A
below 0.15 Hz increases, the loudness of the sound is increased,
and as the height of the central tendency peak below 0.15 Hz
decreases, the loudness of the sound is decreased, albeit in small
proportion than the decreases in loudness. The SCI value for sound
loudness (.theta.lo) is .vertline..DELTA.A.vertline./A.
[0138] In addition, the stimuli duration T.sub.A may be constant or
may vary from approximately 0.12 seconds to approximately 0.16
seconds. The default duration is approximately 0.14 sec. As
sympathetic activity increases, T.sub.A is decreased, and as
sympathetic activity decreases, T.sub.A is increased, albeit in
smaller proportion than the decreases in duration. In other words,
the SCI for T.sub.A is .vertline..DELTA.S.vertl- ine./S. T(off),
which represents the time period between consecutive stimuli, may
be determined in at least one of the following two ways.
[0139] As described above in Example 1, in one embodiment of the
present invention, T.sub.A is not synchronized with any biological
cycle, and T(off) may have any arbitrary predetermined time
value.
[0140] In another embodiment of the present method, the stimuli is
applied during a predefined phase of a physiological cycle, such as
the cardiac cycle. To increase sympathetic activity, the stimuli
will be applied during the early or systolic phase of the cardiac
cycle, where the delay T.sub.W between the fiducial point in the
QRS complex of the ECG signal and the starting point of the stimuli
will be calculated according to equations (1) and (2) explained
above.
[0141] It will be readily apparent to those in the art that
variations of this method and modifications of this apparatus are
possible which fall within the scope of the appended claims.
* * * * *