Method and system for estimation of stress of a person using photoplethysmography

• US 10,881,345 B2
• Filed: 06/02/2017
• Issued: 01/05/2021
• Est. Priority Date: 06/03/2016
• Status: Active Grant

• Current Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Original Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Inventors: Unni, Midhun Parakkal, Jayaraman, Srinivasan, Purushothaman, Balamuralidhar
• Primary Examiner(s): Rivas, Omar F Fernandez
• Assistant Examiner(s): Cocchi, Michael Edward
• Application Number:
  US15/611,953
• Publication Number:
  US 20170360351A1
• Time in Patent Office: 1,313 Days
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/4884 inducing physiological or psychological stress, e.g. applications for stress testing
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A system and method for determining a stress level of a person is provided. The system creates a numerical solution to mathematical model of a blood pressure (BP) regulation, lumped mathematical model of a radial artery and partial differential equation (PDE) model of the radial artery. These models are then used to generate an inference engine using the mathematical model of blood pressure (BP) regulation, the lumped mathematical model of the radial artery and the PDE model of the radial artery. The inference engine is trained using an artificial neural network technique. At the same time the PPG signal of the person is sensed and preprocessed. The preprocessed PPG signal is then given to the trained inference engine. The trained inference engine generates a stress parameter corresponding to the person based on the processed PPG signal.

Claims

1. A method for estimating stress of a person, the method comprising:

creating, by a processor, a numerical solution to a mathematical model of a blood pressure (BP) regulation, wherein the BP regulation model is based on variations induced in a photo-plethysmograph (PPG) signal by kidney or baroreceptors, wherein the BP regulation model takes baroreceptor firing rate and baroreflex arc parameter into consideration, wherein the BP regulation model includes autonomic nervous system firing rate with both long term and short term regulation of BP, wherein the autonomic nervous system firing rate is determined by monitoring a cortisol level of the person, and the determined cortisol level is added to the baroreflex arc parameter;

creating, by the processor, a numerical solution to a lumped parameter model of a radial artery, wherein the lumped parameter model is to convert an output of One-Dimensional Equations (ODEs) at one end of an artery into velocity as experienced at the one end of the artery;

creating, by the processor, a numerical solution to a partial differential equation (PDE) model of the radial artery, the PDE model corresponding to an effect of interaction of heart as a pump with fluid structure interactions of arterial system on the PPG signal;

generating, by the processor, an inference engine using the mathematical model of blood pressure (BP) regulation, the lumped parameter model of the radial artery and the PDE model of the radial artery;

training, by the processor, the inference engine using an artificial neural network technique, wherein the training results in a trained inference engine;

sensing the PPG signal of the person using a PPG sensor;

removing a baseline wandering of the PPG signal;

extracting, by the processor, Fourier components of the PPG signal to generate a preprocessed PPG signal;

providing, the preprocessed PPG signal to the trained inference engine; and

generating a quantitative estimate of stress in the person by the trained inference engine based on the processed PPG signal, wherein the generated quantitative estimate of stress is a real number which is indicative of the stress experienced by the person.

2. The method of claim 1 further includes providing a set of patient parameters to the trained inference engine to personalize the stress estimation method.

3. The method of claim 1 further includes filtering the PPG signal using a Butterworth second order band pass filter which passes frequencies between 0.5-25 Hz.

4. The method of claim 1, further comprising a step of comparing the stress determined for two or more persons.

5. The method of claim 1, wherein the PPG signal is captured from the radial artery of the person.

6. The method of claim 1, wherein the PPG signal is further preprocessed to normalize the Fourier component of the PPG signal.

7. The method of claim 1, wherein the stress is measured in terms of frequency components using a measurement matrix.

8. A system for estimating a stress level of a person in real time, the system comprising:

a PPG sensor for sensing a photo-plethysmograph (PPG) signal of a person;

a preprocessor configured to preprocess the PPG signal to generate a processed PPG signal; and

a memory;

a processor in communication with the memory, wherein the processor configured to perform the steps of;

creating a numerical solution to a mathematical model of a blood pressure (BP) regulation, wherein the BP regulation model is based on variations induced in a photo-plethysmograph (PPG) signal by kidney or baroreceptors, wherein the BP regulation model takes baroreceptor firing rate and baroreflex arc parameter into consideration, wherein the BP regulation model includes autonomic nervous system firing rate with both long term and short term regulation of BP, wherein the autonomic nervous system firing rate is determined by monitoring a cortisol level of the person, and the determined cortisol level is added to the baroreflex arc parameter,

creating a numerical solution to a lumped parameter model of a radial artery, wherein the lumped parameter model is to convert an output of One-Dimensional Equations (ODEs) at one end of an artery into velocity as experienced at the one end of the artery,

creating a numerical solution to a partial differential equation (PDE) model of the radial artery, the PDE model corresponding to an effect of interaction of heart as a pump with fluid structure interactions of arterial system on the PPG signal,

generating an inference engine using the mathematical model of blood pressure (BP) regulation, the lumped parameter model of the radial artery and the PDE model of the radial artery,

training the inference engine using an artificial neural network technique, wherein the training results in a trained inference engine, and

generating a quantitative estimate of stress in the person by the trained inference engine based on the processed PPG signal, wherein the generated quantitative estimate of stress is a real number which is indicative of the stress experienced by the person.

9. The system of claim 8 further includes a step of preprocessing the PPG signal, wherein the preprocessing comprises:

extracting, by the preprocessor, Fourier components of the PPG signal;

filtering, by the preprocessor, the PPG signal using a Butterworth second order band pass filter which passes frequencies between 0.5-25 Hz;

removing, by the preprocessor, baseline wandering from the extracted Fourier components of the PPG signal to generate a processed PPG signal; and

normalizing the Fourier component of the PPG signal.

10. The system of claim 8, wherein the PPG sensor is standalone unit or attached to a mobile phone.

11. A non-transitory computer-readable medium having embodied thereon a computer program for estimating stress of a person, the method comprising:

creating, by a processor, a numerical solution to mathematical model of a blood pressure (BP) regulation, wherein the BP regulation model is based on variations induced in a photo-plethysmograph (PPG) signal by kidney or baroreceptors, wherein the BP regulation model takes baroreceptor firing rate and baroreflex arc parameter into consideration, wherein the BP regulation model includes autonomic nervous system firing rate with both long term and short term regulation of BP, wherein the autonomic nervous system firing rate is determined by monitoring a cortisol level of the person, and the determined cortisol level is added to the baroreflex arc parameter;

creating, by the processor, a numerical solution to a lumped parameter model of a radial artery, wherein the lumped parameter model is to convert an output of One-Dimensional Equations (ODEs) at one end of an artery into velocity as experienced at the one end of the artery;

creating, by the processor, a numerical solution to a partial differential equation (PDE) model of the radial artery, the PDE model corresponding to an effect of interaction of heart as a pump with fluid structure interactions of arterial system on the PPG signal;

generating, by the processor, an inference engine using the mathematical model of blood pressure (BP) regulation, the lumped parameter model of the radial artery and the PDE model of the radial artery;

training, by the processor, the inference engine using an artificial neural network technique, wherein the training results in a trained inference engine;

sensing the PPG signal of the person using a PPG sensor;

removing a baseline wandering of the PPG signal;

extracting, by the processor, Fourier components of the PPG signal to generate a preprocessed PPG signal;

providing, the preprocessed PPG signal to the trained inference engine; and

generating a quantitative estimate of stress in the person by the trained inference engine based on the processed PPG signal, wherein the generated quantitative estimate of stress is a real number which is indicative of the stress experienced by the person.