ESTIMATING CARDIAC PARAMETERS WHEN PERFORMING AN ACTIVITY USING A PERSONALIZED CARDIOVASCULAR HEMODYNAMIC MODEL

20220287572

17/490425

September 30, 2021

The present disclosure enables personalized cardiac rehabilitation guidance and care continuum using a personalized cardiovascular hemodynamic model that effectively simulates cardiac parameters when the patient performs an activity using a wearable device like a digital watch that can help capture Electrocardiogram (ECG) signal, Photoplethysmogram (PPG) signal and accelerometer signal. The cardiovascular hemodynamic models of the art are not personalized and cannot be input with real time parameters from the subject being monitored. Input parameters including Systemic Vascular Resistance (SVR) using Metabolic EquivalenT (MET) levels associated with an activity level of the subject, unstressed blood volume using an autoregulation method, total blood volume in a body of the subject, and heart rate of the subject are estimated and input to the personalized cardiovascular hemodynamic model to estimate cardiac parameters including cardiac output, ejection fraction and mean arterial pressure.

Tata Consultancy Services Limited (Mumbai, IN)

1 A processor implemented method comprising the steps of: estimating, via one or more hardware processors, a plurality of input parameters for a personalized cardiovascular hemodynamic model associated with a subject being monitored, the plurality of input parameters comprising (i) Systemic Vascular Resistance (SVR), (ii) unstressed blood volume, (iii) total blood volume in a body of the subject, and (iv) heart rate of the subject, the step of estimating comprises: estimating the SVR using Metabolic EquivalenT (MET) levels associated with an activity level of the subject; and updating the unstressed blood volume estimated when the subject is at rest, using an autoregulation method, when an activity is performed by the subject, wherein the autoregulation method comprises: sensing aortic pressure by baroreceptors located at carotid sinus and aortic arch; converting the sensed aortic pressure into a neural firing frequency via afferent sympathetic pathways; generating sympathetic and parasympathetic nervous activities via a central nervous system and efferent pathway depending on the neural firing frequency; and updating an additional blood demand representing the unstressed blood volume during the activity using the generated sympathetic and parasympathetic nervous activities; and estimating, via the personalized cardiovascular hemodynamic model, cardiac parameters including cardiac output, ejection fraction and mean arterial pressure, using the estimated plurality of input parameters.

2. The processor implemented method of claim 1, wherein the step of estimating a plurality of input parameters is preceded by personalizing a cardiovascular hemodynamic model to obtain the personalized cardiovascular hemodynamic model, the personalizing being based on one or more of (i) cardiac parameters obtained from an echocardiogram, (ii) ECG signal obtained from a wearable device worn by the subject when performing the activity and (iii) metadata of the subject including height and weight associated thereof.

3. The processor implemented method of claim 2, wherein the step of estimating a plurality of input parameters comprises sequential activation of a right atrium ra, a left atrium la, a right ventricle rv and a left ventricle lv of the personalized cardiovascular hemodynamic model using compliance functions Cra(t), Cla(t), Crv(t) and Clv,(t) respectively, the compliance functions being defined as: the compliance function to actuate ra, [mathematical expression included] [mathematical expression included] and Cmax,ra are the minimum and maximum values of the ra compliance, u(t) is the activation function, time t is considered over a complete cardiac cycle, Ta is the start of the activation of ra and T is the end of the cardiac cycle; (ii) the compliance function to actuate la, Cla(t)=Cmin,la+0.5×(Cmax,la−Cmin,la)u(t−dla), wherein Cmin,la and Cmax,la are the minimum and maximum values of the la compliance and dla represents a time delay between activation of the ra and the la; and (iii) the compliance functions to actuate rv and lv are represented as [mathematical expression included] [mathematical expression included] Ci; i ∈{lv, rv} is the systolic compliance across lv or rv and is estimated as a ratio of R-peak and T-peak of the ECG signal, uv(t) is the activation function, d represents the time delay in activation of lv or rv from ra, and T1 and T2 are the systolic and diastolic activation time instances of the cardiac cycle respectively.

4. The processor implemented method of claim 1, wherein the total blood volume is estimated using height and weight of the subject.

5. The processor implemented method of claim 1, wherein the step of estimating the SVR is represented as [mathematical expression included] wherein Rs(0) represents the SVR at rest, MET(t) represents a metabolic equivalent of the activity performed at the tth time, and τ is a time constant, and wherein the SVR corresponds to a section of the body of the subject depending on the activity being performed, while the SVR of remaining sections are considered constant or modulated by the autoregulation method, the section of the body being an upper body, a middle body or a lower body of the subject.

6. The processor implemented method of claim 1, wherein the heart rate of the subject is estimated using (i) a Photoplethysmogram (PPG) signal and an accelerometer signal or (ii) an Electrocardiogram (ECG) signal, from a wearable device worn by the subject when performing the activity.

7. A system comprising: one or more data storage devices operatively coupled to one or more hardware processors and configured to store instructions for execution via the one or more hardware processors to: estimate a plurality of input parameters for a personalized cardiovascular hemodynamic mod& associated with a subject being monitored, the plurality of input parameters comprising (i) Systemic Vascular Resistance (SVR), (ii) unstressed blood volume, (iii) total blood volume in a body of the subject, and (iv) heart rate of the subject, wherein estimating the plurality of input parameters comprises: estimating the SVR using Metabolic EquivalenT (MET) levels associated with an activity level of the subject; and updating the unstressed blood volume estimated when the subject is at rest, using an autoregulation method, when an activity is performed by the subject, wherein the autoregulation method comprises: sensing aortic pressure by baroreceptors located at carotid sinus and aortic arch; converting the sensed aortic pressure into a neural firing frequency via afferent sympathetic pathways; generating sympathetic and parasympathetic nervous activities via a central nervous system and efferent pathway depending on the neural firing frequency; and updating an additional blood demand representing the unstressed blood volume during the activity using the generated sympathetic and parasympathetic nervous activities; and estimate cardiac parameters including cardiac output, ejection fraction and mean arterial pressure, using the estimated plurality of input parameters.

8. The system of claim 7, wherein the one or more processors are configured to personalize a cardiovascular hemodynamic model to obtain the personalized cardiovascular hemodynamic model, prior to estimating the plurality of input parameters, based on one or more of (i) cardiac parameters obtained from an echocardiogram, (ii) ECG signal obtained from a wearable device worn by the subject when performing the activity and (iii) metadata of the subject including height and weight associated thereof.

9. The system of claim 8, wherein the one or more processors are configured to perform sequential activation of a right atrium ra, a left atrium la, a right ventricle rv and a left ventricle lv of the personalized cardiovascular hemodynamic model using compliance functions Cra(t), Cla, Crv(t), Crv(t) and Clv(t) respectively, the compliance functions being defined as: the compliance function to actuate ra, [mathematical expression included] [mathematical expression included] and Cmax,ra are the minimum and maximum values of the ra compliance, u(t) is the activation function, time t is considered over a complete cardiac cycle, Ta is the start of the activation of ra and T is the end of the cardiac cycle; (ii) the compliance function to actuate la, Cla(t)=Cmin,la+0.5×(Cmax,la−Cmin,la)u(t−dla), wherein Cmin,la and Cmax,la are the minimum and maximum values of the la compliance and dla represents a time delay between activation of the ra and the la; and (iii) the compliance functions to actuate rv and lv are represented as [mathematical expression included] [mathematical expression included] Ci; i ∈{lv,rv} is the systolic compliance across lv or rv and is estimated as a ratio of R-peak and T-peak of the ECG signal, uv(t) is the activation function, d represents the time delay in activation of lv or rv from ra, and T1 and T2 are the systolic and diastolic activation time instances of the cardiac cycle respectively.

10. The system of claim 7, wherein the one or more processors are configured to estimate the total blood volume using height and weight of the subject.

11. The system of claim 7, wherein the one or more processors are configured to estimate the SVR based on [mathematical expression included] wherein Rs(0) represents the SVR at rest, MET (t) represents a metabolic equivalent of the activity performed at the tth time, and τ is a time constant, and wherein the SVR corresponds to a section of the body of the subject depending on the activity being performed, while the SVR of remaining sections are considered constant or modulated by the autoregulation method, the section of the body being an upper body, a middle body or a lower body of the subject.

12. The system of claim 7, wherein the one or more processors are configured to estimate the heart rate of the subject using (i) a Photoplethysmogram (PPG) signal and an accelerometer signal or (ii) an Electrocardiogram (ECG) signal, from a wearable device worn by the subject when performing the activity.

13. A computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: estimate a plurality of input parameters for a personalized cardiovascular hemodynamic model associated with a subject being monitored, the plurality of input parameters comprising (i) Systemic Vascular Resistance (SVR), (ii) unstressed blood volume, (iii) total blood volume in a body of the subject using height and weight of the subject, and (iv) heart rate of the subject using (i) a Photoplethysmogram (PPG) signal and an accelerometer signal or (ii) an Electrocardiogram (ECG) signal, from a wearable device worn by the subject when performing the activity, wherein estimating the plurality of input parameters comprises: estimating the SVR using Metabolic EquivalenT (MET) levels associated with an activity level of the subject; and updating the unstressed blood volume estimated when the subject is at rest, using an autoregulation method, when an activity is performed by the subject, wherein the autoregulation method comprises: sensing aortic pressure by baroreceptors located at carotid sinus and aortic arch; converting the sensed aortic pressure into a neural firing frequency via afferent sympathetic pathways; generating sympathetic and parasympathetic nervous activities via a central nervous system and efferent pathway depending on the neural firing frequency; and updating an additional blood demand representing the unstressed blood volume during the activity using the generated sympathetic and parasympathetic nervous activities; and estimate cardiac parameters including cardiac output, ejection fraction and mean arterial pressure, using the estimated plurality of input parameters,

14. The computer program product of claim 13, wherein the computer readable program further causes the computing device to personalize a cardiovascular hemodynamic model to obtain the personalized cardiovascular hemodynamic model, prior to estimating a plurality of input parameters, the personalizing being based on one or more of (i) cardiac parameters obtained from an echocardiogram, (ii) ECG signal obtained from a wearable device worn by the subject when performing the activity and (iii) metadata of the subject including height and weight associated thereof.

15. The computer program product of claim 14, wherein the computer readable program further causes the computing device to (a) estimate a plurality of input parameters by sequential activation of a right atrium ra, a left atrium la, a right ventricle rv and a left ventricle lv of the personalized cardiovascular hemodynamic model using compliance functions Cra(t), Cla(t), Crv(t) and Clv(t) respectively, the compliance functions being defined as: the compliance function to actuate ra, [mathematical expression included] [mathematical expression included] Cmin, ra and Cmax,ra are the minimum and maximum values of the ra compliance, u(t) is the activation function, time t is considered over a complete cardiac cycle, Ta is the start of the activation of ra and T is the end of the cardiac cycle; (ii) the compliance function to actuate la, Cla(t)=Cmin,la+0.5×(Cmax,la−Cmin,la)u(t−dla), wherein Cmin,la and Cmax,la are the minimum and maximum values of the la compliance and dla represents a time delay between activation of the ra and the la; and (iii) the compliance functions to actuate rv and lv are represented as [mathematical expression included] [mathematical expression included] Ci; i ∈{lv,rv} is the systolic compliance across lv or rv and is estimated as a ratio of R-peak and T-peak of the ECG signal, uv(t) is the activation function, d represents the time delay in activation of lv or rv from ra, and T1 and T2 are the systolic and diastolic activation time instances of the cardiac cycle respectively; and (b) estimate the SVR as [mathematical expression included] wherein Rs(0) represents the SVR at rest, MET(t) represents a metabolic equivalent of the activity performed at the tth time, and τ is a time constant, and wherein the SVR corresponds to a section of the body of the subject depending on the activity being performed, while the SVR of remaining sections are considered constant or modulated by the autoregulation method, the section of the body being an upper body, a middle body or a lower body of the subject.

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