Autonomic nervous system tracker

Abstract

A method for real-time tracking of autonomic nervous system (ANS) activity across all'three branches of the ANS, including the sympathetic nervous system (SNS) and both branches of the parasympathetic nervous system (PNS-DVC and PNS-WC), the method comprising: identifying incoming signals into a computer system from one or more physiological sensors attached to a subject, wherein the incoming signals correspond to one or more physiological responses controlled by the ANS of the subject; extracting one or more features from each incoming physiological signal, wherein the extracted feature is linked to a physiological response regulated by an ANS branch.

Description

AUTONOMIC NERVOUS SYSTEM TRACKERFIELD OF THE INVENTION

[0001] The present disclosure relates generally to autonomic nervous system assessment and tracking.BACKGROUND

[0002] The autonomic nervous system (ANS) governs the function of most organs, thus playing a major role in systemic diseases such as heart failure, diabetes, metabolic disorders etc. R. Bankenahally and H. Krovvidi, BJA Education, vol. 16, no. 11, pp. 381-387, 2016; S.C. Malpas, Physiologial Reviews, vol. 90, no. 2, pp. 513-557, 2010. One branch of the ANS, the sympathetic branch (SNS), prepares the body for ‘fight or flight’ by increasing heart rate, arterial pressure and blood flow. The other parasympathetic branch (PNS) prepares the body for ‘rest and digest’ by slowing the central nervous system and increasing the activity of abdominal viscera. M.L.K., American J of Pharmaceutical Education, vol 71, no. 4, 2007. Psychological features at both trait (e g. personality) and state level (e.g. anxiety) are linked to ANS and contribute further to disease pathology. W.J. Kop, et al., Biological Psychology, vol 86, no. 3, p. 230, 2011.

[0003] Assessing and targeting the ANS is thus a fundamental aspect of treating many conditions and is likely to translate to improved patient care. The ability of the body to switch from a sympathetic dominant state to a parasympathetic dominant state will indirectly determine the overall health of the body. Currently, there are no reliable tools that can detect these ongoing, internal shifts in ANS state that are so essential to human health.

[0004] ANS assessment can be performed non-invasively by recording heart rate and blood pressure responses during tasks such as deep breathing, handgrip or valsalva maneuver to trigger parasympathetic and sympathetic responses. Power spectral analysis of physiological signals(heart rate variability or blood pressure variability) is widely used for ANS assessment. R. Bankenahally and H. Krovvidi, BJA Education, vol. 16, no. 11, pp. 381-387, 2016; D.J. Ewing and B.F. Clarke, British Med. J. (Clinical research ed.), pp. 916-918, 1982. Currently, these analyses are performed post hoc and require manual editing and significant statistical work and specialized expertise for proper analysis. Estimates of ANS activity are inherently noisy due to the complex interaction of multiple sources of change in the sensor features (some are due to ANS changes, but others are noise sources in their applications, such as patient movement generating an artifact in the feature set). Further, currently, the time course for validated analyses of the ANS are typically measured in weeks or potentially days. Thus, currently ANS assessments are primarily a research and not a clinical tool.

[0005] Automated software is available to try to circumvent these issues but, to date, these software tools are less reliable and have been shown to contribute to discrepant research data. B.G.E., Frontiers in physiology, vol. 26, no. 4, 2007. There is no currently available tool that can accurately and simply track the changes in ANS in real-time. The ability to assess ANS state and output data in real-time would have substantial implications for the mechanistic understanding of the role of ANS in a patient’s disease process and in direct management of the patient’s disease treatment. The present disclosure addresses these needs.SUMMARY OF THE INVENTION

[0006] A first aspect of the invention includes a method for real-time tracking of autonomic nervous system (ANS) activity of a subject.

[0007] A second aspect of the invention includes a system for real-time tracking of autonomic nervous system (ANS) activity of a subject.

[0008] A third aspect of the invention includes a system for collecting and managing data from physiological sensors for use in tracking changes in autonomic nervous system (ANS) activity of a subject in real-time.

[0009] A first embodiment is a method for real-time tracking of autonomic nervous system (ANS) activity of a subj ect, the method including the steps of identifying incoming signals into a computer system from one or more physiological sensors attached to a subject, wherein the incoming signals correspond to one or more physiological responses controlled by the ANS of the subject; validating the incoming signals; calibrating the incoming signals; pre-processing the incoming signals to remove noise and unwanted frequency components; extracting one or more features from each incoming physiological signal, wherein the extracted feature is linked to a physiological response regulated by an ANS branch; mapping the extracted features to establish a 0-mean for each extracted feature; comparing the extracted feature against the mapping for the feature to create a feature score; weighting the sum of feature scores for each feature; and displaying the weighted feature score sum for each feature overtime as part of its corresponding ANS branch on a graphical interface.

[0010] A second embodiment is a method for real-time tracking of autonomic nervous system (ANS) activity of a subject where the physiological responses controlled by the ANS collectively include responses controlled by the sympathetic nervous system (SNS), the DVC -parasympathetic nervous system and the WC-parasympathetic nervous system.

[0011] A third embodiment is a method for real-time tracking of autonomic nervous system (ANS) activity of a subject where the incoming signals include one or more of the physiological responses from the group consisting of record heart rate (HR), blood pressure (BP), electrodermal activity (EDA), respiration and photoplethysmography (PPG).

[0012] A fourth embodiment is a method for real-time tracking of autonomic nervous system (ANS) activity of a subject where method of claim 1 wherein the incoming signal is collected for calibration during a time when the subject is at rest.

[0013] A fifth embodiment is a method for real-time tracking of autonomic nervous system (ANS) activity of a subject where the incoming signal is collected for calibration during a time immediately following the subject engaging in activity initiating SNS activation and PNS withdrawal in the subject.

[0014] A sixth embodiment is a method for real-time tracking of autonomic nervous system (ANS) activity of a subject where the physiological signal is down sampled.

[0015] A seventh embodiment is a method for real-time tracking of autonomic nervous system (ANS) activity of a subject where The method of claim 1 wherein the extraction of features from the physiological signals occurs during the time when the subject is engaged in at least one challenge to the ANS.

[0016] An eighth embodiment is a method for real-time tracking of autonomic nervous system (ANS) activity of a subject where the challenge is selected from the group consisting of mental challenges, physical activity challenges, temperature challenges, and pharmacological challenges.

[0017] A ninth embodiment is a method for screening a subject for changes in autonomic nervous system (ANS) activity of a subject, the method comprising a) identifying incoming signals into a computer system from one or more physiological sensors attached to a subject, wherein the incoming signals correspond to one or more physiological responses controlled by the ANS of the subject, b) validating the incoming signals, c) calibrating the incoming signals, d) pre-processing the incoming signals to remove noise and unwanted frequency components, e) extracting one or more features from each incoming physiological signal, wherein the extracted feature is linked to a physiological response regulated by an ANS branch, f) mapping the extracted features toestablish a O-mean for each extracted feature, g) comparing the extracted feature against the mapping for the feature to create a feature score, h) weighting the sum of feature scores for each feature, i) displaying the weighted feature score sum for each feature over time as part of its corresponding ANS branch on a graphical interface

[0018] A tenth embodiment of the method wherein steps e through i are repeated while the subject is undergoing a preselected challenge activating the subject’s ANS response.

[0019] An eleventh embodiment of the method further comprising applying a therapeutic treatment to the subject while the subject is undergoing a preselected challenge activating the subject’s ANS response.

[0020] A twelfth embodiment is a method for monitoring changes in the autonomic nervous system (ANS) activity of a subject f, the method comprising a) identifying incoming signals into a computer system from one or more physiological sensors attached to a subject, wherein the incoming signals correspond to one or more physiological responses controlled by the ANS of the subject, b) validating the incoming signals, c) calibrating the incoming signal s,d) pre-processing the incoming signals to remove noise and unwanted frequency components, e) extracting one or more features from each incoming physiological signal, wherein the extracted feature is linked to a physiological response regulated by an ANS branch, f) mapping the extracted features to establish a 0-mean for each extracted feature, g) comparing the extracted feature against the mapping for the feature to create a feature score, h) weighting the sum of feature scores for each feature, i) displaying the weighted feature score sum for each feature over time as part of its corresponding ANS branch on a graphical interface, and j) repeating steps e through i while the subject is undergoing a preselected challenge activating the subject’s ANS response.

[0021] A thirteenth embodiment wherein a therapeutic treatment is applied to the subject during stepj.BRIEF DESCRIPTION OF THE FIGURES

[0022] The accompanying drawings are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments, and together with the description serve to explain the principles of the disclosure.

[0023] FIG. 1 is a schematic diagram illustrating the components, the data input and an exemplary output of the ANS Tracking system.

[0024] FIG. 2 is a simplified flowchart of Phase I of the system for resolving time-varying degrees of regulatory control over peripheral physiology by the three distinct components of the autonomic nervous system and making that information available to users through a visual display.

[0025] FIG. 3 is a simplified flowchart of Phase II of the system for resolving time-varying degrees of regulatory control over peripheral physiology by the three distinct components of the autonomic nervous system and making that information available to users through a visual display.

[0026] FIG. 4 is a schematic diagram illustrating the use of the autonomic nervous system tracker in screening and / or monitoring of ANS response in a subject.

[0027] FIG. 5 is a schematic diagram illustrating the use of the autonomic nervous system tracker in the screening of a patient for potential treatment.

[0028] FIG. 6 is a simplified diagram illustrating the potential sensors, the parameters that can be extracted therefrom, and an illustrative user interface displaying the PNS and SNS score derived from the parameter extraction.

[0029] FIG. 7 is a bar graph illustrating the correspondence of a physiological sensor data with the PNS and the weighting by the ANS tracking system relative to that correlation.

[0030] FIG. 8 is a bar graph illustrating the correspondence of a physiological sensor data with the SNS and the weighting by the ANS tracking system relative to that correlation.DETAILED DESCRIPTION

[0031] ANS plays an essential role in maintaining body homeostasis. The Polyvagal Theory is an evolutionary model of the nervous system and its relation to the human brain-body connection. S.W. Porges, Psychophysiology, pp. 301-318, 1995; S.W. Porges, Biol Psychol, vol. 74, no. 2, pp. 116-143, 2007; S.W. Porges, Cleveland Clinic Journal of Medicine, pp. 86-90, 2009. The traditional model suggests that, after high sympathetic reactivity from a stressor, the parasympathetic system manages a return to normal homeostasis. J. Clarke and S. Gans, VeryWellMind, 2019 (http: / / www.verywellmind.com / polyvagal-theory-4588049). However, strictly informed by physiology, the Polyvagal Theory presents that the parasympathetic branch can be subdivided into the dorsal and ventral vagal complex (DVC and VVC, respectively).

[0032] Either the SNS or the PNS branch is always dominating based on the internal and external conditions. R. Bankenahally and H. Krovvidi, BJA Education, vol. 16, no. 11. Pp 381- 387, 2016. At ‘rest’ the 3 branches of the ANS (SNS, PNS-DVC, and PNS-VVC) have different levels of neural activity. SNS dominates during emergency ‘fight or flight’ stress reactions or strenuous physical conditions. PNS dominates in relaxed and resting conditions, to allow for energy conservation and to regulate visceral function such as digestion, urination etc. Nominally, the PNS-VVC should be far more active in a safe, relaxed environment. The ability of the body to switch from sympathetic dominance to parasympathetic dominance state will indirectly determine the overall health of the body. However, ANS imbalance is considered the underlying cause for various diseases such as dysautonomias, diabetic neuropathy and cyclic vomiting syndrome.

[0033] The disclosed ANS Tracking System is informed by the Polyvagal Theory of a hierarchical arrangement of the ANS:1. Primary system (newest to appear in evolution of humans) = PNS-VVC2. Secondary system = sympathetic or fight-flight system = SNS3. Tertiary system (oldest component of ANS) = PNS-DVCThis is a key distinction of the present disclosure over the prior assessment systems utilized to date, as the disclosed system does not treat the ANS as a two-component model (sympathetic and parasympathetic nervous systems) but rather divides the parasympathetic branch in the ventral / dorsal complexes and suggests an order to their interaction (higher systems inhibit and regulate lower systems).

[0034] As shown in the diagram of FIG. 1, the ANS Tracking System described herein takes input from a Hardware / Data Acquisition System 105, which may include one or more physiological measurement devices (sensors). The physiological measurement devices may include wired or wireless physiological sensors as well as physiological sensors integrated within wearable devices. The system 200 derives the changing state of the autonomic nervous system (ANS) from the physiological data signals and provide the ANS state of the subject in real time 115. The ANS Tracking System may utilize the computer processing 112 of desktop, laptop, or mobile device and may access information contained in databases stored locally or externally.

[0035] From the sensor input, the system extracts data specific to the selected features. Features are the most discriminating characteristics in signals in the time and frequency domain. For example, in an electrocardiogram (ECG or EKG) signal, R- peaks are located and have at least two features, the time of the R-wave and the amplitude of the ECG voltage at the R- wave peak. Features are a ‘data reduction’ step in the proposed algorithms; a high-density signal like the ECG is reduced to a smaller set of meaningful features. To calculate the R-R interval orInter beat intervals (IBI), the time feature is used to extract a second level feature (TIME R 2 - TIME R l) = IB I I . IBIs are further analyzed in different frequency bands with division into high (0.12-1 Hz) and low frequency (0.04-0.10 Hz) bands. The high frequency component is known as Respiratory Sinus Arrythmia (RSA), which is related to stimulation of vagus nerve. Features are extracted by numerous methods including finding patterns in signals, finding change points, locating peaks, and identifying trends. And features can also be extracted from other features as in theexample above.

[0036] Each feature parameter is tracked across a fixed interval that includes a return to homeostasis. Based on the variance of the fixed interval of time, mean and standard deviation of each feature parameter will be converted to a z-score. The basal level will be fixed at ‘5’ on a 1- 10 scale. Z-transformed parameters will be quantified across stressors (challenges) applied to the patient. The average response to each challenge will be fixed at +2.5 on the SNS scale and -2.5 on the PNS scales. Estimates of three ANS components (SNS, PNS-VVC and PNS-DVC) are then plotted in real-time with lags all set to 0 and the weighted average being based on existing literature in psychophysiology. The weighting is also updated through the post hoc analysis conducted by the ANS tracking system based upon the patient data received through the feature extraction and the ongoing ANS branch scoring of that feature extraction data. The disclosed tool displays an individual patient’s current ANS state and directly inform the medical provider.

[0037] The process implemented by the ANS Tracking System 200 is set out in the diagrams of FIG. 2 and FIG. 3 utilizes two phases. In phase I, as illustrated in FIG. 2, the sensor data is identified, validated, and calibrated. Unless bad signal quality detected, each of the Phase I steps occurs only once. Phase I of the ANS Tracking System 200 comprises the following steps and components:Sensor Identification 230: During the sensor identification, the system utilizes a matching filter to match the incoming signal 225 with a previously created database of exemplar signals, such as an existing template of the original signals, and automatically label each available channel as, for example, ECG or PPG. Pattern matching may be performed using machine learning or statistical models. The system will identify the sampling rate of each channel. Sensor Validation 240: In order to minimize artifacts in the incoming signal, the system will assess if the incoming signal is of good quality against a frequency domain of signalnoise estimates and / or degree of agreement with matched filter template. Signal quality may be affected by wrong placement of sensors or movements of the subject. Poor signal quality can introduce artifacts making the signal unreliable for further analysis.Therefore, if poor signal quality is detected 245, the user will be notified by the system so that corrective measures may be taken. For example, if the poor signal quality is due to the placement of sensors or movement detected, these issues can be corrected by the user of the system. Calibration 255: The calibration step involves collection of sensor input data 250 from the subject / patient for use in signal pre-processing and baseline setting. This step establishes the individuals expected range of signal parameters for the session. Furthermore, the calibration data is saved and or stored in memory for calculating the baseline correction factor. 257. Signal Pre-processing 160: This step involves down sampling the physiological signals to avoid computational complexity as well as smoothing / denoising to remove theunwanted trend in the signal and then removing the unwanted high and low frequency components to extract the desired features from it.In Phase II of the ANS Tracking System 200, as illustrated in FIG. 3, the physiological sensor data is continuously collected, processed, feature extracted, mapped, weighted and provided as a visual output. These steps are repeated throughout the duration of the physiological data collection on the subject / patient. Phase II of the ANS Tracking System 200 comprises the following steps and components:5. Feature extraction 270: Each physiological signal will be decomposed into one or more features that are linked to regulation by the ANS. The feature data is also saved and / or stored in memory and utilized in calculation of the z-scores.6. Feature Mapping 280: All features are mapped to a common, unitless space. The disclosed system maps all features to a 0-mean, 1-standard deviation unit (Z-score).7. ANS branch scores calculated 290: A weighted sum of Z-scores is generated for the extracted features for each PNS and SNS branches. A standard starting state would be weighted 80% PNS to 20% SNS. However, weights for each feature relevant to an ANS branch are based on empirical research and may be modified post hoc based upon additional information for continual improvement in the weighting utilized by the system. 285. As shown in FIG. 7 and FIG. 8, the weighting of the same extracted features may be very different for PNS versus SNS. For example, RSA would be weighted 0.7 for its correlation with activity of PNS but only 0.25 for its correlation with activity of SNS during the same time period while the patient is in the same status.8. Output 115: The real time visual output of the system includes graphical display of the physiological sensor data updated with current estimates of each ANS branch activation.9. ANS Challenges / Treatment 300: During Phase II of the ANS Tracker System, the subject / patient may be subjected to and / or participate in different challenges, stimuli and / or treatments intended to activate one or more aspects of the subject / patient’ s ANS. The type of challenge and / or treatment applied is dependent up whether the ANS Tracker System is being utilized for screening and / or monitoring of the subject / patient.

[0038] The data provided by the output of the ANS Tracker System will inform the user and / or healthcare professional of the changes in ANS activity of the subject in real-time. This will provide opportunity to evaluate the impact of specific conditions and / or treatments upon the subject’s ANS activity in the moment.

[0039] Physiological Sensors and Sensor Selection

[0040] Physiological sensors for use in the disclosed system must meet selection criteria based upon the feature of interest to be extracted. For example, for ECG data, a sensor with a sampling rate of 500hz or less may be sufficient; however, if skin sympathetic nerve activity (SKNA) data is sought, then a higher sampling rate, such as 2500Hz may be preferred. Additionally, placement of the sensor may also impact the feature data that can be extraction.

[0041] Each sensor and location provide a degree of unique and a degree of redundant information on the dynamic processes involved in ANS regulation. Some sensors provide exclusively information on one branch of the ANS while others reflect information from multiple ANS pathways. Physiological measurement devices capable of use with the disclosed invention include micro pressure sensors (Biopac, Goleta, CA) but those of skill in the art will appreciate that a variety of real-time sensors may be used, including electroencephalography (EEG) sensors, functional Near-Infrared Spectroscopy (INIR) sensors, and biochemical sensors.

[0042] Preferably, the physiological sensors utilized in the disclosed system and process will record heart rate (HR), blood pressure (BP), electrodermal activity (EDA), respiration and photoplethysmography (PPG), thus accounting for the interaction of both parasympathetic and sympathetic input. The signals provided by the physiological sensors correspond to one or more of the physiological responses controlled by the ANS of the subject.

[0043] Dynamic action of the VVC can be quantified through measuring respiratory sinus arrhythmia (RSA), during one or more posture challenges (supine / sit / stand etc.). RSA is a validated metric and the primary component of high-frequency heart rate variability. G.F. Lewis, et al., Biological Psychology, vol. 89, no. 2, pp 349-364, 2012. We have observed a link between RSA dynamics during posture shifts and traumatic stress in a study of military unit leaders. Vagal Efficiency (VE) measures the efficiency of the vagal brake assessed from the covariation of RSA magnitude and heart rate over short (10 second) periods. M. Kahfa|3, et al., European Journal of Psychotraumatology, vol. 12, no. 1, 2021. VE thus provides an index of how much heart rate would change with specific increase / decrease in RSA.

[0044] Because the heart reflects both branches of the nervous system, data regarding heart function, such as an ECG, is a preferred physiological data input into the ANS tracking system. Pulse data is complementary to the ECG data and preferred to be used in combination with ECG data. Respiratory data provides independent information from the type provided by the ECG. Therefore, it is preferred to combine respiratory data with the ECG data. Mos preferably, the physiological sensors utilized provide data regarding ECG, blood pressure and respiration. Additional sensors may be utilized to provide electrodermal activity, impedance cardiography data or pupillometry data.

[0045] Those of skill in the art will appreciate that even identical sensors, placed in different locations, can provide some limited unique information on the ANS activity. For example, an optical pulse measure placed on the fingertip can be used to estimate vasomotor activation, which is both a local (part of the limb) and a global (part of the whole body) phenomenon. Having sensors on a finger and a toe, provides redundant information on the global activation of the vasomotor response but unique information on local vascular bed responses.

[0046] While the disclosed ANS Tracking System will function with only 1-2 sensors, precision of the system can be increased as additional physiological measures are added. Those of skill in the art will appreciate that additional sensors may be used to improve the confidence intervals. Some additional sensors may also be used for ANS assessment relating to specific diseases. For example, a gastric motility sensor be added in assessment of gut-brain interaction.

[0047] The physiological sensors may transmit signals via wireless radio transmission, Bluetooth® or similar formatted transmission or via cable connection coupled to a computer to receive the corresponding signal input for use in the ANS Tracking System 200. Upon receiving the signal input, the system will identify 230 and validate the sensor signal. 240. If the signal input fails validation 245, the user of the system will be instructed to restart the process and / or to reposition the selected sensor that failed validation.

[0048] Sensor Validation 240

[0049] The system will assess data over a short time frame to assess the quality of the sensor signal. A time between 30 seconds and 3 minutes should be adequate for signal quality to be assessed. Preferably, about 30 seconds of data will be used for this step.

[0050] If the sensor input signals is determined to be poor 245, the system will provide the user will the option to restart from the beginning or exclude specific sensors / channels of information.

[0051] During the sensor validation step, the system can optionally incorporate demographic information of the subject / patient, such as age, sex, weight, medications, physiological conditions, and / or genetic conditions, that will inform the calibration steps, if such information is available to the system. The information may be provided to the ANS Tracker System through user input or acquired from a database containing the subject / patient information.

[0052] Calibration 250

[0053] Data may be collected while the subject is breathing at normal pace and is seated still. Alternatively, the data may be collected after the patient has completed a series of activities designed to initiate the ANS and shift the ANS slightly towards SNS activation and PNS withdrawal. For example, a patient may complete a series of at least three supine-sit-stand posture shifts prior to attaching the sensors. Data for calibration is then collected during the short recovery period after the postural shifts.

[0054] This establishes the individuals expected range of signal parameters for the session. Preferably, approximately 1-3 minutes of data is collected during the calibration step. However, a person of skill in the art would appreciate that more or less time may be utilized depending upon the stringency desired.

[0055] Calibration data is utilized for the signal pre-processing and it is also saved for calculating the baseline correction factor 257 for use in the signal pre-processing 260.

[0056] Signal Pre-Processing 260

[0057] This step involves down sampling the physiological signals to avoid computational complexity, for example the ECG signal may be down sampled to 5Hz while the respiration signal may be down sampled to 50Hz.

[0058] Feature Extraction 270

[0059] During the feature extraction step 270, each physiological signal will be decomposed into one or more features that are linked to regulation by the ANS. For example, the ECG signal from the sensor will be used to extract beat to beat intervals (EBIs), and from the IBIs estimates of high- and low-frequency HRV magnitude will be extracted. Feature sets for all signals will be extracted at this stage.

[0060] While not inclusive of all features / parameters that may be extracted from the physiological sensor data, examples of additional parameters / features for extraction are shown in FIG. 6. When the ANS Tracking System 200 is used on a subject 100, the sensors selected provide signal input into the ANS Tracking System from which features and parameters of the signal may be extracted 270. The user interface then displays the mapped and scored feature data as applicable to a respective ANS branch.

[0061] The feature extraction 270 may be conducted while the patient is undergoing one or more challenges, stimuli or treatments to elicit SNS activation and PNS withdrawal. Challenges to elicit SNS activation may include, but are not limited to, mental challenges, such as mental arithmetic; physical challenges, such as hand grip or physical movement; temperature challenges, such as cold pressor arm wrap; or pharmacological challenges. The selection of the challenge to elicit the SNS activation and PNS withdrawal is tailored to the branch of the ANS that data is being collected.

[0062] The feature extraction data is saved for use in mapping 280 and generating the weighted score for real-time display. The saved feature extraction data may also be utilized in post-hoc analysis using modified weights 285 applied to the score generation, as additional information and understanding the weighting is developed. This post hoc analysis will inform the system of updates to the weights to be applied to the feature extraction data.

[0063] Feature Mapping 280

[0064] The extracted feature data is mapped to standardized scores. The disclosed system maps all of the extracted features to a 0-mean, 1 -standard deviation unit. Mapping signals to a common range enables integration across multiple oblique representations of ANS branch regulatory control despite different units and numerical ranges. Feature mapping allows integration of multiple noisy indicators of ANS activity in order to derive the best possible estimate of the ANS activity at any moment in time. While Z-scoring is one method utilized for this mapping, those of skill in the art would appreciate that other methods of standardizing may also be used.

[0065] Generating Scores for ANS Branch Reactivity 290

[0066] A weighted sum of Z-scores is generated. Weights for each ANS branch are separate and are based on empirical research. By basing the weighting of the z-scores upon empirical research and published literature, the weighting can be adjusted as additional information on the correlation between the ANS activation and the specific disease being assessed is known.

[0067] An exemplary transformation for the parameter RS A would be as follows: RSAz(x) = (RSA(x) - RSA calibration mean ) / RSA calibration SD

[0068] The 'PNS' and 'SNS' activation scores are created per task. The activation scores are compared with these standardized scores. The correlation with feature:: scores is compared thestandardized scores to determine relative weights (highest correlation — > greatest weight in that ANS branch).

[0069] ANS Challenges / Treatment 300

[0070] The ANS Tracker System may be utilized to screen subjects / patients for ANS response to specific conditions, treatments or stimuli. Those of skill in the art would appreciate that screening for ANS response in a patient may be useful in the diagnostic process for that patient. As shown in FIG 4., an exemplary example of the use of the ANS Tracker System for screening could include subjecting the patient being screened to posture shift challenges during Phase II. If the patient displays vagal parameters within a predetermined range as being positive for potential treatment, then the treatment is applied to the subject and the subject’s ANS changes in response to the treatment are monitored through the ANS Tracker System visual output. Based upon the changes to the subject’s ANS response to the treatment, the treatment may be continued, modified or discontinued.

[0071] The real-time capturing, analyzing, and display of the physiological data by the ANS tracking system provides feedback for adjusting the treatment parameters to improve the effectiveness of the treatment.

[0072] Display / Graphical Interface 115

[0073] The graphical display is updated with current estimates of each ANS branch activation.

[0074] A user can specify parameters of use case at this point, namely how much data they want to visualize on the screen and any alerts / thresholds. Example: 1 -minute scrolling display for detection of acute activation or 20-minute scrolling display for long-term monitoring of recovery. Users may also adjust the update frequency of the display (e.g., 5-seconds or 1-minute between updates).

[0075] Example 1: Use of the disclosed ANS tracking system for patient screening

[0076] In one embodiment of the ANS Tracker System, a patient diagnosed with cyclic vomiting syndrome is screened for vegal efficiency (VE) for potential treatment of they condition using percutaneous electrical nerve field stimulation (PENFS) therapy. VE provides a unique index of heart rate change relative to change in vagal efferent outflow (measured by respiratory sinus arrythmia (RSA) in response to dynamic posture shifts, which require barosensory feedback. Thus, VE is a dynamic measure of brainstem vagal afferent signalling on vagal efferent regulation.

[0077] As shown in FIG. 5, a patient being screened for potential PENFS treatment is subjected to posture shift and / or meal ingestion challenges during Phase II use of the disclosed ANS tracking system. Prior to the posture and / or meal challenges, the ECG, photoplethysmography (PPG), and Respiration sensors are applied to the patient. The signals from these physiological sensors is input to the ANS tracking system and the signals are validated and calibrated as part of Phase I. Features extracted from these physiological sensors include patient’s heart R-R interval, heart rate (HR), HRV, pulse amplitude, blood pressure (BP), inhalation period and exhalation period. These extracted features are mapped to z scores and weighted.

[0078] The posture shifting and / or meal ingestionl challenges activate the patient’s autonomic nervous system and alter the HR, BP, and PPG data received. The ANS tracking system provides real time information on the changes to the PNS-VVC, SNS, and PNS-DVC of the patient during the challenge. In a normal subject, the heart rate (HR) measured in beats per minute (bpm) will increase and the respiratory sinus arrythmia (RSA)(Ln(ms2) will decrease when a subject moves from a sitting to a standing position and the return back to statis when the subject moves from standing to sitting. The vegal efficiency of the subject can then be determined by the slope of theregression line of 10s estimates of mean beat to beat heart period (ms) over the magnitude of high frequency HRV or RS A in Ln(ms2)). “heart period (ms) / (Ln(ms2))”.

[0079] The patient is then subjected to PENFS treatment. The PENFS treatment may be applied during or after the patient is undergoing the posture shift and / or meal ingestion challenges. The ANS tracking system provides real time information on the changes to the branches of the activated ANS and the vegal efficiency of the patient after application of the PENFS treatment.

[0080] By the real-time capturing, analyzing, and display of the physiological data, the ANS tracking system provides feedback for adjusting the PENFS device parameters to improve the effectiveness of the PENFS treatment.

[0081] Example 2: Use of the disclosed ANS tracking system in mental health counselling

[0082] Another use of the disclosed ANS tracking system in the mental health field. Mental health counseling is a dynamic, social interaction between two or more individuals, often a therapist and patient. One major objective in therapy is to teach the pateint skills to self-regulate arousal during difficult situations, such as after being triggered by an event or when feeling anxious or depressed. During therapy, patients discuss difficult topics and are often shifted into an abnormal autonomic state (e.g., anxiety = heightened SNS activity). Therapists use these moments to instruct the client on self-regulatory mechanisms that can help both in that moment and later when a similar or more stressful event happens outside of therapy. Through the real time visual display of the ANSTracker, a patient may visualize the shifts in autonomic activity they are experiencing.

[0083] A patient might use the visualized their changes in ANS activity of one or more branches either as an instructional aide (e.g., “see what happened here to your fight-flight system when we were discussing your job interview?”) or as a form of biofeedback (“It looks like you arebecoming more sympathetically aroused when we talk about your job interview. Let’s pause here and take some deep breaths. Do you see how your brain-body system is rebalancing when we take the deep breaths? When you feel this anxiety building, you can use deep breaths to help bring yourself back to a healthier state.”). In such uses, the patient may not be subjected to separate challenges to activate their ANS, but instead observe or recognize when topics, verbal challenges or memories activate their ANS through the real time output of the ANS tracker system.EQUIVALENTS AND SCOPE

[0084] Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation many equivalents to the specific embodiments described herein. The scope of the present invention is not intended to be limited to the above, but rather is as set forth in the appended claims.

[0085] In the claims articles such as “a,” “an,” and “the” may mean one or more than one unless indicated to the contrary or otherwise evident from the context. Claims or descriptions that include “or” between one or more members of a group are considered satisfied if one, more than one, or all of the group members are present in, employed in, or otherwise relevant to a given product or process unless indicated to the contrary or otherwise evident from the context. The invention includes embodiments in which exactly one member of the group is present in, employed in, or otherwise relevant to a given product or process. The invention includes embodiments in which more than one, or all of the group members are present in, employed in, or otherwise relevant to a given product or process.

[0086] Furthermore, it is to be understood that the invention encompasses all variations, combinations, and permutations in which one or more limitations, elements, clauses and descriptive terms, from one or more of the listed claims is introduced into another claim. Forexample, any claim that is dependent on another claim can be modified to include one or more limitations found in any other claim that is dependent on the same base claim.

[0087] Where elements are presented as lists, e.g., in Markush group format, it is to be understood that each subgroup of the elements is also disclosed, and any element(s) can be removed from the group. It should be understood that, in general, where the invention, or aspects of the invention is / are referred to as comprising particular elements, features, etc., certain embodiments of the invention or aspects of the invention consist, or consist essentially of, such elements, features, etc. For purposes of simplicity, those embodiments have not been specifically set forth in haec verba herein. It is also noted that the term “comprising” is intended to be open and permits the inclusion of additional elements or steps.

[0088] Where ranges are given, endpoints are included. Furthermore, it is to be understood that unless otherwise indicated or otherwise evident from the context and understanding of one of ordinary skill in the art, values that are expressed as ranged can assume any specific value or subrange within the stated ranges in different embodiments of the invention, to the tenth of the unit of the lower limit of the range, unless the context clearly dictates otherwise.

[0089] The term “about” or “approximately” means within an acceptable error range for the particular value as determined by one of the ordinary skill in the art, which will depend in part on how the value is measured or determined, e.g., the limitations of the measurement system. For example, “about” can mean within 1 or more than 1 standard deviation, per the practice in the art. Alternatively, “about” can mean a range of up to 20%, up to 10%, up to 5% or up to 1 % of a given value. Alternatively, the term can mean within an order of magnitude, for example within 5-fold, or within 2-fold, of a value. Where particular values are described in the application and claims,unless otherwise stated the term “about” meaning within an acceptable error range for the particular value should be assumed.

[0090] In addition, it is to be understood that any particular embodiment of the present invention that falls within the prior art may be explicitly excluded from any one or more of the claims. Because such embodiments are deemed to be known to one of ordinary skill in the art, they may be excluded even if the exclusion is not set forth explicitly herein. Any particular embodiment of the method of the invention can be excluded from any one or more claims, for any reason, whether or not related to the existence of prior art.

[0091] Each of the foregoing patents, patent applications and references is hereby incorporated by reference, particularly for the teaching referenced herein.

Claims

CLAIMSI claim1. A method for real-time tracking of autonomic nervous system (ANS) activity of a subject, the method comprising: identifying incoming signals into a computer system from one or more physiological sensors attached to a subject, wherein the incoming signals correspond to one or more physiological responses controlled by the ANS of the subject; validating the incoming signals; calibrating the incoming signals; pre-processing the incoming signals to remove noise and unwanted frequency components; extracting one or more features from each incoming physiological signal, wherein the extracted feature is linked to a physiological response regulated by an ANS branch; mapping the extracted features to establish a 0-mean for each extracted feature; comparing the extracted feature against the mapping for the feature to create a feature score; weighting the sum of feature scores for each feature; and displaying the weighted feature score sum for each feature over time as part of its corresponding ANS branch on a graphical interface.

2. The method of claim 1 wherein the physiological responses controlled by the ANS collectively include responses controlled by the sympathetic nervous system (SNS), the DVC -parasympathetic nervous system and the WC-parasympathetic nervous system.

3. The method of claim 1 wherein the incoming signals include one or more of the physiological responses from the group consisting of heart rate (HR), blood pressure (BP), electrodermal activity (EDA), respiration and photoplethysmography (PPG).

4. The method of claim 1 wherein the incoming signal is collected for calibration during a time when the subject is at rest.

5. The method of claim 1 wherein the incoming signal is collected for calibration during a time immediately following the subject engaging in activity initiating SNS activation and PNS withdrawal in the subject.

6. The method of claim 1 wherein the extraction of features from the physiological signals occurs during the time when the subject is engaged in at least one challenge to the ANS.

7. The method of claim 6 wherein the challenge is selected from the group consisting of mental challenges, physical activity challenges, temperature challenges, and pharmacological challenges.

8. A method for screening a subject for changes in autonomic nervous system (ANS) activity of a subject, the method comprising: a) identifying incoming signals into a computer system from one or more physiological sensors attached to a subject, wherein the incoming signals correspond to one or more physiological responses controlled by the ANS of the subject; b) validating the incoming signals; c) calibrating the incoming signals; d) pre-processing the incoming signals to remove noise and unwanted frequency components; e) extracting one or more features from each incoming physiological signal, wherein the extracted feature is linked to a physiological response regulated by an ANS branch;f) mapping the extracted features to establish a 0-mean for each extracted feature; g) comparing the extracted feature against the mapping for the feature to create a feature score; h) weighting the sum of feature scores for each feature; i) displaying the weighted feature score sum for each feature over time as part of its corresponding ANS branch on a graphical interface j) repeating steps e through i while the subject is undergoing a preselected challenge activating the subject’ s ANS response.

9. The method of claim 8 further comprising applying a therapeutic treatment to the subject during step j .

10. A method for monitoring changes in the autonomic nervous system (ANS) activity of a subject f, the method comprising: a) identifying incoming signals into a computer system from one or more physiological sensors attached to a subject, wherein the incoming signals correspond to one or more physiological responses controlled by the ANS of the subject; b) validating the incoming signals; c) calibrating the incoming signals; d) pre-processing the incoming signals to remove noise and unwanted frequency components; e) extracting one or more features from each incoming physiological signal, wherein the extracted feature is linked to a physiological response regulated by an ANS branch; f) mapping the extracted features to establish a 0-mean for each extracted feature; g) comparing the extracted feature against the mapping for the feature to create a feature score;h) weighting the sum of feature scores for each feature; i) displaying the weighted feature score sum for each feature over time as part of its corresponding ANS branch on a graphical interface j) repeating steps e through i while the subject is undergoing a preselected challenge activating the subject’s ANS response.

11. The method of claim 10 further comprising applying a therapeutic treatment to the subject during stepj.

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