U.S. patent application number 15/344970 was filed with the patent office on 2017-05-11 for non-invasive physiological quantification of stress levels.
The applicant listed for this patent is LifeQ Global Limited. Invention is credited to Franco Bauer du Preez, Alida Fanfoni, Shannagh Jane Hare, Laurence Richard Olivier.
Application Number | 20170127993 15/344970 |
Document ID | / |
Family ID | 58662381 |
Filed Date | 2017-05-11 |
United States Patent
Application |
20170127993 |
Kind Code |
A1 |
Olivier; Laurence Richard ;
et al. |
May 11, 2017 |
Non-Invasive Physiological Quantification of Stress Levels
Abstract
A data acquisition device includes measuring instruments to
generate physiological and/or psychological data streams.
Microprocessors within the acquisition device process the generated
data streams into metrics, which feed into stress function
algorithms. Algorithm processing may occur either on the device, or
metrics may be communicated via wireless communication for external
processing on mobile devices and/or cloud-based platforms. The
calculated stress functions inform cloud-based computational
systems biology-derived models describing the dynamics of hormones
and neurotransmitters released in the body in response to stressful
stimuli. Stress hormone levels are quantified using these models,
and are used in combination to serve as biologically inspired
metrics of acute and chronic stress an individual is
experiencing.
Inventors: |
Olivier; Laurence Richard;
(Alpharetta, GA) ; du Preez; Franco Bauer;
(Cobham, GB) ; Hare; Shannagh Jane; (Cape Town,
ZA) ; Fanfoni; Alida; (Stellenbosch, ZA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
LifeQ Global Limited |
Dublin |
|
IE |
|
|
Family ID: |
58662381 |
Appl. No.: |
15/344970 |
Filed: |
November 7, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62251996 |
Nov 6, 2015 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/02055 20130101;
A61B 5/08 20130101; A61B 5/4041 20130101; A61B 5/6831 20130101;
A61B 5/0402 20130101; A61B 5/0533 20130101; A61B 5/4035 20130101;
A61B 5/02416 20130101; A61B 5/6824 20130101; A61B 2562/0223
20130101; A61B 2560/0242 20130101; A61B 5/6826 20130101; A61B
2562/0219 20130101; A61B 5/6833 20130101; A61B 5/0816 20130101;
A61B 5/165 20130101; A61B 5/6814 20130101; A61B 5/6861 20130101;
A61B 5/11 20130101; A61B 5/0476 20130101; A61B 5/14546 20130101;
A61B 5/4884 20130101; A61B 5/6823 20130101; A61B 5/6828 20130101;
A61B 5/7264 20130101; A61B 5/021 20130101; A61B 5/4227 20130101;
A61B 5/053 20130101 |
International
Class: |
A61B 5/16 20060101
A61B005/16; A61B 5/11 20060101 A61B005/11; A61B 5/0205 20060101
A61B005/0205; G06F 19/24 20060101 G06F019/24; A61B 5/00 20060101
A61B005/00 |
Claims
1. A system for non-invasive physiological and psychological
quantification of stress levels of a human subject comprising the
following: a. a data acquisition device including: i. a
non-invasive instrument for acquiring physiological and
psychological data from the human subject; ii. a microcontroller
for processing of the acquired physiological and psychological data
into biological metrics; b. a communication link; and c. a
computing platform connected to the communication link for: i.
receiving the biological metrics from the data acquisition device;
and ii. processing the biological metrics into quantifiable stress
metrics by means of a stress function algorithm module to determine
the level of stress experienced by the human subject.
2. The system of claim 1, wherein the measuring instruments are
selected from a group of data acquisition devices including an
implant device, an ingestible device, a nanotechnology device, a
chest strap, a chest patch, a head band, an upper arm band, an
upper arm patch, a wrist band, a finger band, a finger patch, an
arm sleeve, and a leg sleeve.
3. The system of claim 1, wherein the algorithm modules are
selected from the group including a K-means clustering analysis
module, a fuzzy clustering module, a Gaussian mixture model module,
a stress rotation model module, and an ensemble of the K-means
cluster analysis module, the fuzzy clustering module, the Gaussian
mixture model module, and the stress rotation model module.
4. The system of claim 1, wherein the computing platform further
calculates an estimate of hormones or neurotransmitters released by
the human subject based on the biological metrics and wherein the
estimate of hormones or neurotransmitters released by the human
subject relates to the level of stress experienced by the human
subject.
5. The system of claim 4, wherein the computing platform uses the
biological metrics to estimate hormones and/or neurotransmitters
released by the human subject during activation of a SAM stress
axis.
6. The system of claim 4, wherein the computing platform uses the
biological metrics to estimate hormones and/or neurotransmitters
released by the human subject during activation of the HPA stress
axis.
7. The system of claim 4, wherein the stress level experienced by
the human subject includes average stress levels, levels of acute
stress, levels of chronic stress, stress intensity, duration of
stressor, levels of distress, and levels of eustress.
8. The system of claim 1, wherein the computing platform is a
cloud-based computing platform that receives the biological metrics
from the microcontroller of the acquisition device by means of the
communication link.
9. The system of claim 1, wherein the system includes a third-party
database and the quantified stress metrics are sent to the
third-party database by means of the communication link.
10. The system of claim 1, the computing platform for determining
the quantifiable stress metrics from the biological metrics resides
on a mobile device connected to the data acquisition device by the
communication link.
11. The system of claim 1, the computing platform for determining
the quantifiable stress metrics from the biological metrics resides
on the data acquisition device.
12. A method for non-invasive physiological and psychological
quantification of stress levels of a human subject comprising the
following: a. acquiring physiological and psychological data from
the human subject by means a non-invasive instrument of a data
acquisition device; b. sending the acquired physiological and
psychological data to a computing platform via a communication
link; c. receiving the acquired physiological and psychological
data from the data acquisition device; and d. processing the
acquired physiological and psychological data into quantifiable
stress metrics by means of a stress function algorithm module to
determine the level of stress experienced by the human subject.
13. The method of claim 1, wherein the acquired physiological and
psychological data is: a. converted by a microprocessor of the data
acquisition device into biological metrics; b. receiving the
biological metrics from the data acquisition device; and c.
processing the biological metrics into quantifiable stress metrics
by means of a stress function algorithm to determine the level of
stress experienced by the human subject.
14. The method of claim 12, wherein the measuring instruments are
selected from a group of data acquisition devices including an
implant device, an ingestible device, a nanotechnology device, a
chest strap, a chest patch, a head band, an upper arm band, an
upper arm patch, a wrist band, a finger band, a finger patch, an
arm sleeve, and a leg sleeve.
15. The method of claim 12, wherein the algorithm are selected from
the group including a K-means clustering analysis, a fuzzy
clustering, a Gaussian mixture model, a stress rotation model, and
an ensemble of the K-means cluster analysis, the fuzzy clustering,
the Gaussian mixture model, and the stress rotation model.
16. The method of claim 12, wherein the processing step further
calculates an estimate of hormones or neurotransmitters released by
the human subject based on the biological metrics and wherein the
estimate of hormones or neurotransmitters released by the human
subject relates to the level of stress experienced by the human
subject.
17. The method of claim 16, wherein the processing step uses the
biological metrics to estimate hormones and/or neurotransmitters
released by the human subject during activation of a SAM stress
axis.
18. The method of claim 16, wherein the processing step uses the
biological metrics to estimate hormones and/or neurotransmitters
released by the human subject during activation of the HPA stress
axis.
19. The method of claim 16, wherein the stress level experienced by
the human subject includes average stress levels, levels of acute
stress, levels of chronic stress, stress intensity, duration of
stressor, levels of distress, and levels of eustress.
20. The method of claim 12, wherein the level of stress is
communicated to a third-party database by means of the
communication link.
Description
CROSS REFERENCE TO RELATED PATENT APPLICATIONS
[0001] This invention claims priority from U.S. Provisional Patent
Application No. 62/251,996, filed Nov. 6, 2015, which is hereby
incorporated by reference.
FIELD OF THE INVENTION
[0002] The present invention relates to the field of non-invasive
digital health monitoring and signal processing. Specifically, the
present invention relates to a system and method for non-invasive
quantification of stress levels in a human subject.
BACKGROUND OF THE INVENTION
[0003] Stress is a fundamental problem in today's society. While
young and healthy individuals may be able to handle bouts of acute
stress, continual exposure to acute as well as chronic stress may
have long-term effects that are harmful to health. A need exists to
monitor stress levels to allow for better stress management and
therefore a reduction in negative health effects related to
stress.
[0004] Stress is a complex physiological and psychological
phenomenon. Although a number of physiological and psychological
stress measurements exist, no single gold standard metric for the
quantification of acute and/or chronic stress exists. Existing
stress measurements are considered too simplistic and/or invasive
and/or hard to measure, and therefore, existing stress measurements
do not offer reliable and/or continuous monitoring of stress
levels. Challenges therefore remain to be able to quickly,
continuously, accurately and non-invasively monitor an individual's
stress levels in order to identify stressors, which will enable
better management and improvement of overall stress levels and
general well-being.
Definition of Stress
[0005] Biological or physiological stress is defined as the
response of an organism to a stressor, such as an environmental
condition or stimulus. Stress can be positive (eustress),
exemplified by, but not limited to, exercise, or negative
(distress), exemplified by, but not limited to, work stress. With
either stress, the body reacts accordingly to overcome each
respective challenge or situation. Acute stress is defined as
short-term stress driven by a specific situation, which can be
interpreted as exciting and/or motivating (for example a
rollercoaster ride) or scary (for example a near car accident).
Stress can further be subdivided into psychological stress and
physical or exercise stress. Chronic stress occurs when there is
repeated exposure to a stressful situation, which leads to chronic
activation of the stress response.
Control of Stress on a Biological Level
[0006] The stress response is controlled by the limbic areas of the
brain such as the hippocampus, amygdala, and the hypothalamus, the
portion in the brain that links the nervous system to the endocrine
system via the pituitary gland. When an environmental sensory
stimulus is perceived as a stressor by the limbic-prefrontal cortex
of the brain, nerve impulses travel from the hippocampus and
activate the posterior hypothalamus, which in turn trigger the
sympathetic branch of the autonomous nervous system. This neural
stress axis is the first to respond in reaction to a stressor, and
it directly innervates target organs such as the heart to increase
heart rate and cardiac output, as well as skeletal muscle
activation. Noradrenaline is the main hormone released by the
synaptic neurons that innervate these organs. The parasympathetic
branch of the autonomous nervous system acts as counterbalance to
rapid stress responses, and maintains homeostasis by the secretion
of neural transmitters such as acetylcholine, which will lead to a
decrease in heart rate and cardiac output.
[0007] In addition to direct innervation of target organs via the
sympathetic nervous system, two main stress response pathways
exist: the Sympathetic-Adreno-Medullary (SAM) axis and the
Hypothalamus-Pituitary-Adrenal (HPA) axis. Activation of the SAM
axis is a fast acting physiological response, and leads to an
increased level of catecholamine hormones in the blood plasma and
serum, including the "fight-or-flight" hormones adrenaline and
noradrenaline. When a stressor has been processed by the
limbic-pre-frontal cortical interface of the brain, the hippocampus
is stimulated, with subsequent stimulation of the hypothalamus
followed by the adrenal medulla. Stimulation of the adrenal medulla
results in a release of adrenaline and noradrenaline into the
bloodstream, which activates the "fight-or-flight" response.
Adrenaline is the primary hormone released at this stage,
comprising 80% of the total secretion. This physiological response
is rapid, and causes an increase in heart rate, blood pressure,
cardiac output, sweating, and blood glucose levels. Crosstalk
exists between the SAM axis and the HPA axis, because cortisol can
also stimulate the medulla of the adrenal gland to produce
adrenaline and noradrenaline. Once the stress has been dealt with,
the parasympathetic nervous system is activated to inhibit the
effect of the sympathetic nervous system, and homeostasis is
restored.
[0008] Activation of the HPA axis is a relatively slow
physiological response, and leads to an increased level of steroid
hormones, including cortisol and aldosterone. Activation of the HPA
axis starts with the release of corticotropin-releasing hormone
(CRH) from the hypothalamus. CRH acts on the anterior lobe of the
pituitary gland that releases adrenocorticotropic hormone (ACTH)
upon CRH activation. ACTH travels through the bloodstream to
stimulate the cortex of the adrenal gland, which subsequently
results in the release of cortisol and aldosterone. Cortisol is a
key regulator of the stress response and has widespread effects on
the body, with its major roles including the promotion of glucose
formation via gluconeogenesis and the redistribution of energy to
critical organs such as the heart and brain, and furthermore
suppresses unnecessary functions such as the immune response and
reproduction. Cortisol levels peak ten to thirty minutes following
a stressful event and levels can remain elevated for approximately
an hour after the event. Aldosterone is another hormone secreted
from the adrenal cortex, and is part of the renin-angiotensin
system, which regulates blood pressure by retaining sodium and
water in the kidneys. While the body has negative feedback systems
in place to ensure that the body returns to homeostasis after an
acute stress stimulus, exposure to chronic stress causes this
system to be over-stimulated, which may have severe health
implications.
[0009] Other factors also affect the levels of cortisol, including
age, gender, viral infections, sleep deprivation, caffeine
consumption, and intense physical exercise. Cortisol levels are
regulated in a circadian fashion, with cortisol levels subjected to
a diurnal cycle. Cortisol levels are the highest in the early
mornings, with levels decreasing throughout the day until it
reaches its lowest levels at night, between three and five hours
after the onset of sleep. The diurnal regulation of cortisol may
change under abnormal physiological and psychological conditions,
exemplified by, but not limited to, stress.
Current Methods of Stress Measurement
[0010] A number of psychological and physiological stress
measurements exist, although no single gold standard metric for
measuring stress exists. Heart rate variability (HRV) is the most
commonly used measure of acute stress in academic literature as
well as some commercial applications. Variability in heart rate
occurs due to the opposing activities of the sympathetic and
parasympathetic branches of the autonomous nervous system, which
forms part of the SAM stress response pathway. However, it has been
shown that HRV measurements are not a perfect representation of the
sympathetic and parasympathetic systems and that these two systems
are not correlated under all conditions. Research has shown that
using HRV alone as a measurement of stress is an oversimplification
of a complicated physiological process. Other measurements of
stress that give an indication of SAM axis activation include heart
rate, blood pressure monitoring, electrodermal activity
measurement, respiratory rate measurement, and salivary a-amylase
levels. Monitoring and quantification of stress levels is also
achieved via assessment using psychological questionnaires. All of
these measurements are considered too simplistic or invasive and/or
hard to measure and therefore do not offer reliable and/or
continuous monitoring of stress levels.
[0011] Research studies have shown that cortisol levels can be
reliably sampled from saliva and peak ten to thirty minutes after
the induction of stress. The hormone cortisol is a key regulator of
the stress response, and is synthesized from cholesterol in the
adrenal glands. Blood cortisol levels peak ten to thirty minutes
following a stressful event. Levels remain elevated for
approximately one hour after the event. Elevated levels of blood
cortisol activate a negative feedback loop system, which leads to a
reduction of cortisol production causing blood cortisol levels to
return to baseline. This negative feedback mechanism ensures that
the body returns to homeostasis following a stress stimulus. Blood
cortisol levels therefore serve as a biological marker of stress.
Salivary cortisol has therefore become one of the most popular
biomarkers for stress studies, and is the gold standard metric for
activation of the HPA axis. A caveat of salivary cortisol
measurements is that salivary cortisol measurements do not
perfectly compare with blood cortisol levels due to the fact that
some salivary cortisol levels are due to cortisone activity in the
mouth and that samples require laboratory analysis. Therefore, it
is difficult to obtain quick results or to enable continuous
monitoring.
[0012] U.S. Pat. No. 8,622,901 (Jain et. al.) describes a method
for the continuous monitoring of stress in patients using
accelerometer data combined with a number of other sensors
including (but not limited to) heart-rate monitors, blood pressure
monitors, pulse oximeters, and mood sensors. In order to enable
continuous monitoring of stress levels using this method, a
personalized stress profile is created for each individual patient
from renal-Doppler sonography data, where the resistive index (R/1)
of patients are used to calculate stress. A strong correlation
exists between R/1 and self-reported stress levels of patients. The
relationship between R/1 and self-reported stress levels are used
to generate algorithms for calculating a stress index. The stress
index is correlated with physiological and psychological data
streams collected from the above-mentioned sensors, and a stress
model for calculating the stress index as a function of
physiological, psychological, behavioral, and environmental data is
then determined. U.S. Patent Application Publication 2010/0022852
(Westerink et al.) describes a method for processing galvanic skin
response (GSR) signals to estimate the level of arousal of a user.
GSR sensors measure the electrical resistance of the skin. Arousal
of the sympathetic branch of the autonomous nervous system leads to
an increase in sweat gland activity, which leads to an increase in
skin conductance. Skin conductance can therefore be a measure of
stress responses. The particular embodiment describes a computer
program product for processing GSR signals which when run, controls
a computer to estimate a level of arousal. European Patent
Application 2586365 (Sanchez Avila et. al.) describes a method for
quantifying stress in a user, wherein the method allows for
establishing discrimination between stressed users and relaxed
users. The invention uses heart rate and GSR signals as data input,
and utilizes stress patterns based on sigmoid transfer functions to
allow quantifying stress in a larger number of situations. U.S.
Patent Application Publication 2013/0281798 (Rau et al.) discloses
methods for periodically monitoring the emotional state of a
subject. Subjects are exposed to a plurality of stimuli during a
session, wherein data is acquired through a plurality of
physiological and psychological monitoring sensors. Data is
transferred to a database, followed by data processing to extract
objective information about the emotional state of a subject,
specifically pertaining to emotional states including, but not
limited to, anxiety disorder, depression, mood disorder, attention
deficit hyperactivity disorder, autism spectrum disorder, and
bipolar disorder.
[0013] There remains a considerable need for biologically inspired
systems and methods that can accurately, quickly, continuously, and
non-invasively quantify and monitor an individual's stress levels.
As described herein, systems and methods for accurate, continuous
and non-invasive quantification of biological stress levels are
disclosed.
SUMMARY OF THE INVENTION
[0014] The present invention is a physiological and psychological
quantification system that comprises a data acquisition device
including measuring instruments to generate physiological and/or
psychological data streams. A microprocessor within the data
acquisition device processes the generated data streams into
biological metrics that are fed into stress function algorithms.
Algorithm processing may occur either on the data acquisition
device, or the biological metrics may be communicated via a
wireless communication link for external processing on mobile
devices and/or a cloud-based computing platform. The cloud-based
computing platform calculates stress intensity and uses the
calculated stress intensity and biology-derived models to describe
the dynamics of hormones and neurotransmitters released in the body
in response to stressful stimuli. Stress hormone levels are
quantified using the biology-derived models, and are used in
combination to serve as biologically inspired metrics of acute and
chronic stress an individual is experiencing.
[0015] For example, the data acquisition device is attached to the
body of a human subject by, but not limited to, a wrist band, chest
strap, chest patch, head band, upper arm band, upper arm patch,
implant, ingestible, or nanotechnology. The measuring instruments
capture signals exemplified by, but not limited to, physiological
and psychological signals. External or predetermined data or data
streams exemplified by, but not limited to, Doppler sonography
data, psychological assessment data, and captured patient data
during monitoring sessions are not required. The data streams
generated by the measuring instruments are processed by a
microprocessor, contained within the data acquisition device, into
digital measurements that are further processed into biological
metrics. The biological metrics are fed to stress function
algorithms that provide a coarse level prediction of eustress
and/or distress levels, and predictions may be given as stress
intensities (0-100%). The biological metrics may also be obtained
from databases to feed into the stress function algorithms. The
stress function algorithm processing occurs on the data acquisition
device, or the biological metrics are sent via a wireless
communications link to a mobile device with an internet connection,
or to a cloud-based computing platform, for stress function
algorithm processing. Coarse level stress predictions inform
biomathematical stress models that describe the dynamics of
hormones and neurotransmitters released in the body upon stress.
Specific stress hormone levels are estimated from these
biomathematical stress models via stress function algorithm
processing, and the calculated stress hormone levels may serve as
readout for the human subject. The stress hormone and
neurotransmitter estimations may be used in combination to compute
biologically inspired quantifiable metrics of acute and chronic
stress. The quantified stress metrics are relayed back from the
cloud-based computing platform through the wireless communications
link to the mobile devices and/or to the data acquisition device
for display and/or notification to the user of his or her
biological stress levels. The quantified stress metrics and/or the
modeled stress hormone and neurotransmitter estimations can also be
relayed to third party databases and/or mobile devices via an
internet communications link. Examples of third parties include,
but are not limited to, clinical, insurance, and retail parties. A
condensed version of cloud-based biomathematical stress models can
also be sent to the data acquisition device to enable stress
hormone and neurotransmitter estimations and calculations of the
quantifiable stress metrics on the data acquisition device
itself.
[0016] Further objects, features and advantages will become
apparent upon consideration of the following detailed description
of the invention when taken in conjunction with the drawings and
the appended claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] FIGS. 1A and 1B together are a schematic representation
illustrating the stress quantification system in accordance with
the present invention.
[0018] FIG. 2 is a schematic representation of the SAM stress axis,
illustrating the biological control processes modeled by ordinary
differential equations on a cloud-based computing platform in
accordance with the present invention.
[0019] FIG. 3 is a schematic representation of the HPA stress axis,
illustrating the biological control processes modeled by ordinary
differential equations on a cloud-based computing platform in
accordance with the present invention.
[0020] FIG. 4 is a schematic representation of a non-invasive
stress quantification system in accordance with the present
invention.
[0021] FIG. 5 is a chart that illustrates a comparison of measured
salivary cortisol levels and model-predicted cortisol levels of two
different test subjects obtained during a Trier Social Stress Test
(TSST).
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
Glossary of Acronyms
TABLE-US-00001 [0022] ACC Accelerometer ACTH Adrenocorticotropic
Hormone API Application Program Interface BP Blood Pressure BR
Breathing Rate CRH Corticotropin Releasing Hormone CSB
Computational Systems Biology DSP Digital Signal Processing ECG
Electrocardiogram EDA Electrodermal Activity EEG
Electroencephalogram GMM Gaussian mixture model GSR Galvanic Skin
Response GYRO Gyroscope HPA Hypothalamus - Pituitary-Adrenal HRV
Heart rate variability MAG Magnetometer PPG Photoplethysmography
SAM Sympathetic-Adreno-Medullary TRAIT State-Trait Anxiety
Inventory TSST Trier Social Stress Test
[0023] With reference to FIGS. 1A and 1B, a physiological and
psychological stress quantification system 100 measures and
predicts stress for a human subject. The physiological and
psychological stress quantification system 100 comprises a data
acquisition device 101 that is connected to the body of a human
subject by, for example, but not limited to, a wrist band, chest
strap, chest patch, head band, upper arm band, upper arm patch or
via implant, ingestible, or nanotechnology. The data acquisition
device 101 comprises measuring instruments 102, capable of
capturing signals exemplified by, but not limited to, physiological
and psychological signals. Physiological signals may include, but
are not limited to, cardiac, motion, audio, pulmonary, thermal,
electrodermal, and brain signals. Psychological signals may
include, but is not limited to, user feedback, subjective feedback,
and environmental feedback. With reference to FIG. 1A, the
measuring instrument 102 captures physiological data including
cardiac signals 120 (such as photoplethysmography PPG 122,
electrocardiogram ECG 123, and blood pressure BP 124), motion
signals 130 (such as accelerometer ACC 132, magnetometer MAG 133,
and gyroscope GYRO 134), pulmonary signals 140 (such as breathing
rate BR 142), thermal signals 150 (such as temperature 152 and heat
flux 153), EDA signals 160 (such as galvanic skin response GSR
162), and brain signals 170 (such as electroencephalogram EEG
172).
[0024] With further reference to FIG. 1A the measuring instruments
102 also capture psychological data including user feedback 180
(activity tracking 182 and API feedback 183), subjective feedback
190 (TSST 192 and TRAIT 193), and environment feedback 195 (people
196, weather 197, and activity 198).
[0025] Data streams obtained from the measuring instrument 102 are
converted to digital signals by digital signal processing DSP
module 112 of microcontroller 103, or by processing within the
measuring instruments 102, to give measurements 113. The
measurements 113 are subjected to biological based processing by
biological based processing module 114 of the microcontroller 103
to generate biological metrics 104 from the microcontroller 103.
Additional data streams for biological based processing may also be
obtained from databases, for example, but not limited to, medical
and genetic databases. In particular embodiments, the biological
metrics 104 are processed by the microcontroller 103 within the
data acquisition device 101. In other embodiments, the biological
metrics 104 are sent via a wireless communication link 105 to a
mobile device 110 exemplified by, but not limited to, a smartphone,
tablet computer, or laptop computer, with an internet connection
for communication to a cloud-based computing platform 106.
[0026] The biological metrics 104 serve as input for stress
function algorithm module 107 on the cloud-based computing platform
106. In other embodiments, the biological metrics 104 may also
serve as input for stress function algorithm processing on the data
acquisition device 101. In particular embodiments, using the
biological metrics 104 as input for stress algorithm processing
(module 107), either on the data acquisition device 101 or on the
cloud-based computing platform 106, a generalized model is
described that predicts whether a subject is in a state of acute
mental stress or not. In other embodiments, a model is described
that can quantify the level of stress a subject is experiencing
given the data and the biological metrics 104 acquired from the
data acquisition device 101 using statistical methods embodied in
stress function algorithm module 107 to provide a coarse level
prediction of stress intensity (0-100%). In preferred embodiments,
data acquired from the coarse level prediction is used in the
biomathematical model module 108 that quantifies the level of acute
and chronic stress that a subject is experiencing physiologically,
by estimating stress hormone and neurotransmitter levels,
including, but not limited to, adrenaline, noradrenaline,
acetylcholine, CRH, ACTH, cortisol, and aldosterone.
[0027] In particular embodiments, stress hormone and
neurotransmitter estimations are used in combination to serve as
biologically inspired quantifiable metrics of acute and chronic
stress. The quantified stress metrics, such as stress intensity
115, are relayed back to the data acquisition device 101 from the
cloud-based computing platform 106 through the wireless
communication link 105 to the mobile devices 110 (part of the
communication link 105) and/or to the data acquisition device 101
for display and/or notification to the user of his or her
biological acute and/or chronic stress levels. In particular
embodiments the quantified stress metrics, such as stress intensity
115 and/or modeled stress hormone and neurotransmitter estimations
116 can also be relayed to third party databases and/or mobile
devices via internet communications. Examples of third parties
include, but are not limited to, clinical, insurance, and retail
parties. A condensed version of cloud-based
biomathematically-derived stress models 108 can also be sent to the
data acquisition device 101 or the mobile device 110 to enable the
stress hormone and neurotransmitter estimations 116 and
calculations of quantifiable stress metrics on the data acquisition
device 101. Condensed or simplified personal stress models can also
be transmitted to the data acquisition device 101 or the mobile
device 110.
Stress Function Algorithms
[0028] the biological metrics 104 from microcontroller 103
contained within the data acquisition device 101 are sent via the
wireless communications link 105 to the mobile device 110 with an
internet connection for communication to the cloud-based computing
platform 106 for processing by stress function algorithm module
107. In other embodiments, stress function algorithm processing
(module 107) of the biological metrics 104 occurs on the data
acquisition device 101. In particular embodiments, the stress
function algorithms in the stress function algorithm module 107
analyze the biological metrics 104 derived from the microcontroller
103. Particularly, the biological metrics 104 are analyzed by
cluster analysis. Cluster analysis is the act of grouping a set of
objects in such a way that objects in the same group (called a
cluster) are more similar to each other than to those in other
clusters. An example of cluster analysis is k-means clustering 117,
which is used to classify measurements, the biological metrics 104,
derived from the data acquisition device 101 into different stress
and activity level states. Another example of cluster analysis is
fuzzy clustering implemented by fuzzy clustering analysis module
111. Fuzzy logic is a form of computer logic, the output of which
is a continuum of values between 0 and 1 which can also be
represented as 0-100%. The system starts by assigning a set of
membership functions for each input and a set for each output. A
set of rules for the membership function is then applied. In
particular embodiments, the algorithm allows the k-means clusters
(module 117) to inform the shape of the membership function. Fuzzy
clustering (module 111) provides an indication of the percentage of
which of the features in the data belong to a particular cluster or
state. It is therefore possible to determine the level of stress
(from 0-100%) that a subject is experiencing. The output of fuzzy
clustering is a stress function that fluctuates with time as stress
levels rise and fall. A Gaussian mixture model (GMM) module 118 is
another example of clustering model. The GMM model assumes that all
the data points are generated from a finite number of Gaussian
distributions with unknown parameters. In particular embodiments,
the GMM model offers an advantage by combining the clustering
process and the stress function calculation in one model. In
particular embodiments, a stress rotation model module 119 is used
for the classification of acute and chronic stress and exercise
events. Vector directionality of data points on a parametric plot
may be visualized as loops or "rotations". Rotational measurements
may correspond to stress or exercise events. In order to capture
and quantify the information generated by rotations, an algorithm
calculates the area of rotations for both stress and exercise. The
area is output as a stress function indicating the duration and
severity of the stress or exercise event. This model offers an
advantage over clustering techniques in that it has the ability to
predict both acute mental stress and exercise at the same time. In
other embodiments, stress intensities are calculated by using a
combination of K-means clustering, fuzzy clustering GMM, and stress
rotation algorithms. The above-mentioned theory and methods are
used to create an ensemble 121 to classify stress events. Stress
intensities are subsequently calculated using, as an example, but
not limited to, logistic regression functions using a minimum of
one biological metric as input to guide the intensity.
SAM Axis Model (FIG. 2)
[0029] In particular embodiments, stress functions, as determined
from the above-described algorithms (module 107, FIG. 1B), inform
ordinary differential equation (ODE)-based models of the SAM stress
pathway 201 illustrated in FIG. 2. These computational system
biology derived-models (module 108, FIG. 1B) describe the dynamics
of hormones and neurotransmitters that are released in response to
a stressful stimulus and therefore provide insight into the likely
levels of hormones, including, but not limited to, adrenaline,
noradrenaline, and acetylcholine, circulating in an individual's
bloodstream. Parameter inputs that are found in the literature,
such as binding efficiencies of hormone receptors and half-lives of
hormones, together with the outputs from the combination of stress
function algorithms (module 107) as described above, are used as
the major input for ODE models describing the SAM axis. In
particular embodiments, this model provides an estimation of
adrenaline and noradrenaline levels in the body.
HPA Axis Model (FIG. 3)
[0030] In particular embodiments, stress functions, as determined
from the above-described stress function algorithms (module 107),
inform ordinary differential equation (ODE)-based models of the HPA
stress pathway 301 illustrated in FIG. 3. These computational
system biology derived-models (module 108) describe the dynamics of
hormones and neurotransmitters that are released in response to a
stressful stimulus (circadian rhythm+stress stimulus) and therefore
provide insight into the likely levels of hormones, including, but
not limited to, corticotropin-releasing hormone (CRH),
adrenocorticotropic hormone (ACTH), cortisol, and aldosterone
circulating in an individual's bloodstream. Parameter inputs that
are found in the literature, such as binding efficiencies of
hormone receptors and half-lives of hormones, together with the
outputs from the combination of stress function algorithms as
described above, are used as the major input for ODE models
describing the HPA axis model. In particular embodiments, this
model provides an estimation of CRH, ACTH, cortisol, and
aldosterone levels in the body.
Biologically Inspired Quantifiable Metrics of Acute and Chronic
Stress (FIG. 4)
[0031] With reference to FIG. 4, the cloud-based biomathematical
models (108, 201, 301, 402) use estimated stress hormone and
neurotransmitter levels as inputs to calculate combinatory stress
metrics exemplified by, but not limited to, the amount of chronic
and acute stress an individual is experiencing, duration of the
stressor, stress intensities, and average stress levels. In other
embodiments, condensed versions of cloud-based biomathematical
models are sent to a data acquisition device 405 or a mobile device
406, to enable stress hormone and neurotransmitter estimations and
calculations, as well as display, quantifiable stress metrics 402
on the data acquisition device 405. Input for biomathematical
models includes, but is not limited to, the HPA axis input 301, the
SAM axis input 201, and sympathetic and parasympathetic nerve axis
input. In particular embodiments, the quantified metrics 402 are
sent from the cloud-based computing platform 401 to the data
acquisition device 405 or the mobile device 406 as readout via a
wireless communications link 403. In other embodiments, the
quantified metrics 402 may be relayed from the cloud-based platform
401 through the wireless communication link 403 to the mobile
device 406 for display and/or notification to the user of his or
her biological stress levels. Hormone and neurotransmitter
estimations 408 include, for example, acute stress metrics 422,
chronic stress metrics 424, stress intensity 426, duration of
stressor 428, and average stress levels 430. The hormone and
neurotransmitter estimations 408 can also be relayed from the
cloud-based computing platform 401 and/or the mobile device 406 to
third party databases and/or third-party mobile devices 407 via
internet communications link 403. Examples of third parties 407
include, but are not limited to, clinical, insurance, and retail
parties. Contextual information may be inferred from the quantified
stress metrics 402 by third parties 407, to gain insight into
physiological and environmental conditions/stimuli pertaining to
the patient/client.
[0032] FIG. 5 shows examples of comparisons of measured salivary
cortisol levels and HPA model-predicted cortisol levels of two
different test subjects obtained during a Trier Social Stress Test
(TSST).
User Example 1
[0033] In one embodiment of the invention, the data acquisition
device 405 gathers physiological signals from a human subject
wearing the data acquisition device 405. The subject provides
context for stress events 404 (FIG. 4) via brief interactions with
the interface of the data acquisition device 405 or the mobile
device 406. Examples of the context include persons with whom the
patient interacted, meetings, social settings, or any other
information that might have relevance to the occurrence of the
stress state. The subject receives daily, weekly, and monthly
statistics on his or her stress levels. Examples of stress levels
may include, but are not limited to, the average level of stress
and the intensity as well as duration of the stressor, and the
quantifiable stress metrics 402. The more context the user
provides, the more relevant the information provided to the user
can be. The clinical third parties 407 may access the contextual
data on a third party database and/or mobile device 406 to make
physiological and environmental inferences 432 (FIG. 4) to aid in
prescribing relevant medication, improve current prescribed
medications, diagnose new, and/or unknown stressors, and gain
additional insight into the user's overall lifestyle and health for
improved prognoses. FIG. 5 shows a comparison of measured salivary
cortisol levels 412 and model-predicted cortisol levels 414 of two
different test subjects obtained during a Trier Social Stress Test
(TSST). The areas 416 in FIG. 5 indicate where stress is detected,
using stress rotation algorithms. The areas 418 indicate high
stress intensities experienced. The line 420 indicates heart rate,
the solid dots 412 show measured cortisol levels, and the line 414
representing predicted cortisol levels 414 with the HPA axis
model.
User Example 2
[0034] In one embodiment of the invention, the data acquisition
device 405 gathers physiological signals on a psychiatry or
psychology patient and infers periods of likely biological stress.
In lieu of the data acquisition device 405 and the physiological
quantification system 400, such a stressful experience would
typically be analyzed as part of a psychiatry/psychology session
where the patient recalls the context surrounding the event weeks
after the fact (if at all) to aid the clinician towards an optimal
treatment program. With the physiological stress quantification
system 400 of the present invention, the stress state could be
validated or questioned by the user and the context surrounding it
can be gathered later the same day under less stressful
circumstances as identified by the data acquisition device 405
(FIG. 4), using a brief interaction with the mobile device 406 in
contact with the data acquisition device 405 or directly through
the interface of the data acquisition device 405. This has the dual
advantage of capturing the occurrence and context 404 surrounding
stressful events in a way that is not possible in lieu of the
invention. Examples of the context include persons with whom the
patient interacted, meetings, social settings, or any other
information that might have relevance to the occurrence of the
stress state.
[0035] While this invention has been described with reference to
preferred embodiments thereof, it is to be understood that
variations and modifications can be affected within the spirit and
scope of the invention as described herein and as described in the
appended claims.
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