Patent Description:
The disclosure herein generally relates to the field of in-silico modeling of hemodynamic patterns of physiologic blood flow, and, more particularly, to systems and methods for estimating blood pressure of a subject using an electrocardiogram (ECG) driven cardiovascular model.

Computer simulation-based cardiovascular modeling in healthcare is an attractive proposition since analytical models aid in improving understanding of cardiac physiology which in turn is useful for predicting adverse accidents like sudden cardiac death. Clinicians find predictive models useful to stratify likelihood or severity of exercise intolerance in patients. In-silico model platforms also serve as virtual test-beds to verify consequences on different levels of exercise for pathological conditions of varying severity.

Various literatures define cardiac parameter variations based on lumped order models. Conventional cardiovascular hemodynamic models depend on neuromodulation schemes (baroreflex autoregulation) and threshold parameters of neuromodulation correlate with physical activities. Thus these models may not work practically for a large set of people due to dependency on prior knowledge of these parameters. Establishing a cardiac care continuum for cardiac rehabilitation may not be as effective as desired. Document <NPL>", discloses a cardiovascular digital twin platform to simulate the effect of exercise on various cardiac parameters of medical importance. The model incorporates the real-time ECG signal from the body-worn sensors to estimate exercise level and compute cardiac variables like left ventricular dynamics, cardiac output, ejection fraction, mean arterial pressure, etc., of an individual while performing exercises. This document determines the cardiac compliances from the morphology of the single-lead ECG signal and Systemic resistance estimation from the information of the exercise level for a specific subject performing physical activities. Simulation results are produced employing an open-source database (Troika). This document proposed framework that can aid to progressively track the cardiovascular efficiency during exercise for patients with cardiac co-morbidity and act as a personalized therapy guide in a care continuum scenario (Abstract). Document <NPL>" discloses to compare the heart rate (HR) dynamics and variability before and after high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) protocols with workloads based on treadmill workload at which maximal oxygen uptake was achieved (WLVO<NUM>max). Ten participants performed cardiopulmonary exercise testing (CPET) to obtain oxygen uptake (WLVO<NUM>max). All training protocols were performed on a treadmill, with <NUM>% grade, and had similar total distance. The MICT was composed b <NUM> mins at <NUM>% of WLVO<NUM>max. The first HIIT protocol (HIIT-<NUM>: <NUM>) was composed by <NUM> repetitions of <NUM> at <NUM>% of SVO<NUM>max and the second HIIT protocol (HIIT-<NUM>: <NUM>) was composed by three repetitions of <NUM> at <NUM>% of WLVO<NUM>max. Before, during and after each training protocol, HR dynamics and variability (HRV) were analysed by standard kinetics and linear (time and frequency domains). The repeated measures analysis of variance indicated that the HR dynamics, which characterizes the speed of HR during the rest to exercise transition, was statistically (p < <NUM>) slower during MICT in comparison to both HIIT protocols. The HRV analysis, which characterizes the cardiac autonomic modulation during the exercise recovery, was statistically higher in HIIT-<NUM>: <NUM> in comparison to MICT and HIIT-<NUM>: <NUM> protocols (p < <NUM> and p = <NUM>, respectively), suggesting that the HIIT-<NUM>: <NUM> induced higher sympathetic and lower parasympathetic modulation during exercise in comparison to the other training protocols. In conclusion, HIIT-<NUM>: <NUM> demonstrated post-exercise sympathetic hyperactivity and a higher HR peak, while the HIIT-<NUM>: <NUM> and MICT resulted in better HRV and HR in the exercise-recovery transition. The cardiac autonomic balance increased in HIIT-<NUM>: <NUM> while HIIT-<NUM>: <NUM> induced sympathetic hyperactivity and cardiac overload (Abstract).

Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems.

In an aspect, there is provided a processor implemented method comprising estimating, by an in-silico cardiovascular hemodynamic model via one or more hardware processors, in each cardiac cycle of an electrocardiogram (ECG) signal, cardiac parameters based on morphology of the ECG signal associated with a subject, wherein the cardiac parameters include a continuous heart rate (HR) and a set of compliance parameters, and wherein estimating the set of compliance parameters is based on: (i) a set of PQRST amplitudes; and (ii) time-instances, ( <MAT>; j ∈ m) for a jth cardiac cycle (∀j ∈ m) of the ECG signal; generating, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, a set of compliance functions using the estimated cardiac parameters; sequentially activating, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, a plurality of cardiac chambers, in a synchronized manner, using the generated set of compliance functions; and estimating blood pressure of the subject, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, wherein the in-silico cardiovascular hemodynamic model is driven by the ECG signal associated with the subj ect.

In another aspect, there is provided a system comprising a memory storing instructions in an in-silico cardiovascular hemodynamic model; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: estimate, in each cardiac cycle of an electrocardiogram (ECG) signal, cardiac parameters based on morphology of the ECG signal associated with a subject, wherein the cardiac parameters include a continuous heart rate (HR) and a set of compliance parameters, and wherein estimating the set of compliance parameters is based on: (i) a set of PQRST amplitudes; and (ii) time-instances, ( <MAT>; j ∈ m) for a jth cardiac cycle (Vj E m) of the ECG signal; generate, a set of compliance functions using the estimated cardiac parameters; sequentially activate, a plurality of cardiac chambers, in a synchronized manner, using the generated set of compliance functions; and estimate blood pressure of the subject, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, wherein the in-silico cardiovascular hemodynamic model is driven by the ECG signal associated with the subject.

In yet another aspect, there are provided one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause estimating, by an in-silico cardiovascular hemodynamic model via one or more hardware processors, in each cardiac cycle of an electrocardiogram (ECG) signal, cardiac parameters based on morphology of the ECG signal associated with a subject, wherein the cardiac parameters include a continuous heart rate (HR) and a set of compliance parameters, and wherein estimating the set of compliance parameters is based on: (i) a set of PQRST amplitudes; and (ii) time-instances, ( <MAT>]; j ∈ m) for a jth cardiac cycle (∀j ∈ m) of the ECG signal; generating, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, a set of compliance functions using the estimated cardiac parameters; sequentially activating, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, a plurality of cardiac chambers, in a synchronized manner, using the generated set of compliance functions; and estimating blood pressure of the subject, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, wherein the in-silico cardiovascular hemodynamic model is driven by the ECG signal associated with the subj ect.

In the present invention, the one or more hardware processors are configured to estimate HR based on the HR associated with a noise-less ECG signal, when the ECG signal is missing or is noisy, and is represented as: <MAT> where, hae (end), hae (<NUM>) are the HRs at a last and a first instance of capturing the ECG signal respectively, w(t) = H(<NUM>, σ<NUM>) is white-noise with zero-mean, variance of σ<NUM> = <NUM>, and τk = <NUM> sec defines the time constant, σ<NUM>, and τk are learnt empirically through the ECG signal using linear regression.

The invention is defined in the claims.

Cardiovascular diseases (CVD) are a main cause of death worldwide; with coronary heart disease (CHD) accounting for a majority of CVD mortality. CHD has a high prevalence and is aggravated by lifestyle disorders. Exercise-based cardiac rehabilitation is often prescribed as a prevention scheme to reduce the impact of CHD. Cardiac rehabilitation (CR) is a complex secondary preventive intervention that aims to optimize cardiovascular disease risk reduction, promoting adoption and adherence of healthy habits, and reducing disability among those with established CHD. CR is prescribed to patients suffering from cardiac diseases like valvular heart disease, heart transplantation, heart failure with reduced ejection fraction (EF), post-coronary artery bypass grafting (CABG), etc. with the goal of improving quality of life and reducing re-hospitalization. Although CR is a multi-component risk management process, exercise is considered as an integral component. Exercise has been shown to regulate several established CHD risk factors like blood pressure, blood lipid profile, glucose metabolism, weight status and body composition through cardiovascular and metabolic adaptation. In-silico models serve as virtual test beds to verify consequences on different levels of exercise for pathological conditions of varying severity.

Machine learning based conventional cardiovascular hemodynamic models are dependent on prior knowledge of threshold parameters of neuromodulation schemes, thereby limiting their application in establishing a cardiac care continuum for cardiac rehabilitation. Applicants' previous patent application No. <CIT> provided compliance functions for activating cardiac chambers of cardiovascular hemodynamic models, however, the parameters used were constants. The present disclosure enables estimating cardiac parameters from an electrocardiogram (ECG) signal associated with a subject using the morphology of the ECG signal, thereby reproducing activation delays in the cardiac chambers purposefully. In accordance with the present disclosure, the blood pressure of the subject is also estimated using the ECG signal even if the signal is missed for some time instance(s) or is noisy.

Based on the requirement of both exercise monitoring and in-silico modeling for establishing a cardiac care continuum for cardiac rehabilitation, the present disclosure provides cardiovascular digital-twin simulation system as shown in <FIG> that illustrates an exemplary block diagram of a system <NUM> for estimating cardiac parameters when performing an activity using a personalized cardiovascular hemodynamic model, in accordance with some embodiments of the present disclosure. In an embodiment, the system <NUM> includes one or more hardware processors <NUM>, communication interface device(s) or input/output (I/O) interface(s) <NUM>, and one or more data storage devices or memory <NUM> operatively coupled to the one or more hardware processors <NUM>. The one or more hardware processors <NUM> can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, graphics controllers, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) are configured to fetch and execute computer-readable instructions stored in the memory. In the context of the present disclosure, the expressions 'processors' and 'hardware processors' may be used interchangeably. In an embodiment, the system <NUM> can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.

I/O interface(s) <NUM> can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface(s) can include one or more ports for connecting a number of devices to one another or to another server.

The memory <NUM> may include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, one or more modules (e.g. the in-silico cardiovascular hemodynamic model) of the system <NUM> can be stored in the memory <NUM>. <FIG> illustrates an exemplary block diagram of a cardiovascular hemodynamic model, in accordance with some embodiments of the present disclosure while <FIG> illustrates an exemplary flow diagram of a computer implemented method <NUM> for estimating blood pressure of a subject using an electrocardiogram (ECG) driven cardiovascular model, in accordance with some embodiments of the present disclosure. In an embodiment, the system <NUM> includes one or more data storage devices or memory <NUM> operatively coupled to the one or more hardware processors <NUM> and is configured to store instructions configured for execution of steps of the method <NUM> by the one or more hardware processors <NUM>. The memory <NUM> further comprises (or may further comprise) information pertaining to input(s)/output(s) of each step performed by the system and method of the present disclosure. In other words, input(s) fed at each step and output(s) generated at each step are comprised in the memory <NUM> and can be utilized in further processing and analysis, particularly functionalities represented by modules (the in-silico cardiovascular hemodynamic model) illustrated in <FIG>. The modules are implemented as at least one of a logically self-contained part of a software program, a self-contained hardware component, and/or, a self-contained hardware component with a logically self-contained part of a software program embedded into each of the hardware component that when executed perform the method <NUM> described hereinafter. Accordingly, the modules are invoked by the one or more hardware processors <NUM> to perform the method <NUM> of the present disclosure.

The steps of the method <NUM> will now be explained in detail with reference to the components of the system <NUM> of <FIG> and the block diagram of <FIG>. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously.

The human cardiovascular system contains a couple of atrium and ventricles acting like a pulsatile pump. The systemic circulation is produced by the left-ventricle (lv) and left-atrium (lv) pumping oxygenated blood to all body tissues via aorta. On the other side, the right-ventricle (rv) and right-atrium (ra) drive deoxygenated blood to the lungs forming the pulmonic circulation. The rhythmic unidirectional blood flow across the cardiac chambers is controlled by four heart valves, namely, mitral (mi) and aortic (ao) valves in the left heart and tricuspid (tr) and pulmonic (pu) valves in the right heart respectively which are synchronously opened and closed based on the pressure difference across the chambers. Hence, the hemodynamics of the human circulatory system outlines the dynamics of blood flow as a function of pressure-volume fluctuations across cardiac chambers. Additionally, the homeostatic process of autoregulation continuously monitors and regulates blood flow throughout the body. So, to get the quantitative overview of a cardiac system, it is necessary to monitor the hemodynamics of the cardiac chambers such as pressures, volumes, blood-flows, etc..

According to the electrophysiology principle, each of the heart chambers is simultaneously actuated by an autonomous compliance function. During each cardiac cycle, the activation begins in the sinoatrial node located inside the right atrium (ra), then spreads throughout the atrium (depolarization of atria). It then propagates to the ventricles after passing through the atrioventricular node, bundle of His and the Purkinje fibers (depolarization and repolarization of ventricles). As a consequence, during the repolarization state, the ventricles fill with incoming blood from atrium, and in the depolarization state, blood ejects from ventricles. The autonomous activation functions across the cardiac chambers can analytically be defined as compliance functions given below. <MAT> <MAT> <MAT> where, i ∈ {lv, rv}, time t is considered over a complete cardiac cycle (T), Ta, (Ta + dla) are the activation times across (ra) and (la) respectively, and (T<NUM> + d), (T<NUM> + d) define the systolic and diastolic activation times across the ventricles.

Thus, the compliance action of the cardiac chambers is represented as: <MAT> <MAT> <MAT>.

Now, considering equations (<NUM>) - (<NUM>), it is observed that there are several dynamic parameters (such as heart-rate (T), (ra) activation time (Ta), (la) activation time (Ta + dla), ventricular systolic (T<NUM> + d), and diastolic (T<NUM> + d) times. As mentioned above, in Applicants previous patent application No. <CIT>, these parameters were considered as constants. In accordance with the present disclosure, these parameters are estimated in each cardiac cycle of the ECG signal associated with the subject to simulate the in-silico ECG driven cardiovascular hemodynamic model in line with the human cardiovascular system as explained above. It may be noted that in the context of the present disclosure, the expressions "in-silico cardiovascular hemodynamic model", "cardiovascular model" and "cardiovascular hemodynamic model" may be used interchangeably.

Accordingly, in an embodiment of the present disclosure, an in-silico cardiovascular hemodynamic model via the one or more hardware processors <NUM>, is configured to estimate, at step <NUM>, cardiac parameters based on morphology of an electrocardiogram (ECG) signal associated with a subject in each cardiac cycle of the ECG signal. The ECG signal may be captured using sensors in say a wearable device, such as a digital watch. The cardiac parameters include a continuous heart rate (HR) and a set of compliance parameters for each cardiac cycle. <FIG> illustrates an ECG signal with PQRST amplitudes and time steps, as known in the art.

In accordance with the present disclosure, to estimate the continuous HR of the ECG signal, the R-to-R interval of each cardiac cycle is identified as shown in <FIG>. Let us assume that D = [t<NUM>, t<NUM>,. , tm]; <MAT> defines the set containing the durations of the cardiac cycles of the entire ECG signal. Thus, the continuous heart-rate (HR) of the ECG data is HR = <NUM>/D.

In accordance with the present disclosure, the compliance parameters are estimated based on:.

It is often observed that the ECG signal during physical activities (such as exercise) is very noisy. Furthermore, in some window, the ECG signal may be missing in scenarios where the sensor may be disconnected at least momentarily. In such scenarios, the present disclosure enables estimating an approximate HR based on the HR information as measured through noise-less ECG signal. In accordance with an embodiment of the present disclosure, the continuous HR estimation is represented as: <MAT> where, hae (end), hae (<NUM>) are the HRs at a last and a first instance of capturing the ECG signal respectively, w(t) = H(<NUM>, σ<NUM>) is white-noise with zero-mean, variance of σ<NUM> = <NUM>, and τk = <NUM> sec defines the time constant, σ<NUM>, and τk are learnt empirically through the ECG signal using linear regression. <FIG> illustrates an estimated heart-rate of a subject <NUM> of Kaggle dataset (explained later in the description), in accordance with some embodiments of the present disclosure, wherein solid line represents an estimated HR in a missing window (ECG signal is missing) and dotted line represents an estimated HR from the ECG signal. From <FIG>, it is perceived that the HR is within a normal HR range (<NUM>-<NUM> bpm) in a resting state and it starts increasing during an exercising state. Once, the physical activities are completed, it again starts decreasing and approaching towards the normal HR range.

In an embodiment, the set of compliance parameters of the jth cardiac cycle is estimated as: <MAT> wherein,.

Accordingly, in accordance with the present disclosure, the in-silico cardiovascular hemodynamic model via the one or more hardware processors <NUM>, is configured to generate, at step <NUM>, a set of compliance functions using the estimated cardiac parameters as represented by equations (<NUM>-<NUM>) above.

The cardiac chambers are activated sequentially, in a synchronized manner, by the time-varying compliance functions explained above. Typically, this activation starts from the sinoatrial node, which is located inside ra, then, it traverses to the la with a time delay of dla, causing them to contract for pumping the blood into the ventricles. After that, the activation traverses from the atrium to the ventricles via an atrioventricular node with a time delay of d, allowing the ventricles to fill with blood. In accordance with the present disclosure, the in-silico cardiovascular hemodynamic model via the one or more hardware processors <NUM>, is configured to sequentially activate, at step <NUM>, the cardiac chambers of the in-silico ECG driven cardiovascular hemodynamic model, in a synchronized manner, using the generated set of compliance functions. <FIG> illustrates synchronous activation signals to trigger cardiac chambers of an in-silico ECG driven cardiovascular hemodynamic model, in accordance with some embodiments of the present disclosure.

In accordance with the present disclosure, the in-silico cardiovascular hemodynamic model via the one or more hardware processors is configured to estimate blood pressure of the subject, at step <NUM>, wherein the in-silico cardiovascular hemodynamic model is driven by the ECG signal associated with the subject.

A study was performed using Kaggle dataset (provided in "<NPL>et al. ) containing ECG data (signal) for <NUM> subjects (age range of <NUM>-<NUM> years) while exercising on a treadmill. The meta-data information (such as age, height, and weight) is also provided. In this experiment, all subjects were requested to run on a treadmill for <NUM> at <NUM>/hr speed. After completing the exercise, the data collection started immediately. The whole data collection process consumed around <NUM>-<NUM> per subject. Additionally, during data-collection, several reference Blood Pressure (BP) are measured by a traditional BP measurement technique.

As the data-collection step begins immediately after the exercise is over, so, the ECG data during the exercise is missing. To overcome this problem, it was assumed that each subject was initially in a resting state for <NUM> sec, then, performs the treadmill work out for <NUM>. Hence, for this specific time-frame, the heart-rate (HR) needs to be estimated to simulate the cardiovascular hemodynamic model based on equation (<NUM>) above.

For validating responses of the in-silico ECG driven cardiovascular hemodynamic model of the present disclosure, ground truth BP of the Kaggle dataset was utilized. This data contains force-sensing-resistor (FSR) signal for determining the BP estimation time-instances. <FIG> illustrates a force-sensing-resistor (FSR) signal, as known in the art while <FIG> illustrates a simulated blood pressure of the subject <NUM> with respect to the FSR signal of <FIG>, in accordance with some embodiments of the present disclosure. Table 1A and Table 1B below shows a comparative study between the ground truth BP versus simulated BP of various subjects of the Kaggle dataset.

On comparing the ground-truth and simulated BPs (Table 1A and 1B) of different subjects (of varying age and BMI), it is seen that the simulated systolic BP (SPB) and diastolic BP (DBP) are close to the ground truth BP. Additionally, the correlation between the ground-truth BP [systolic (SBP) and diastolic (DBP)] and the measured BP of all the subjects is shown in <FIG>.

In developing countries, coronary heart disease (CHD) is a leading cause of death. Lifestyle disorders are one of the precursors of CHD. Exercise-based cardiac health rehabilitation is often prescribed as a secondary prevention strategy to reduce the impact of CHD. The present disclosure proves an in-silico cardiac platform to simulate the exercise effect on cardiac parameters relevant for cardiac rehabilitation. The method and system of the present disclosure enable simulating an individual's (subject) exercise condition using the ECG signal approximated from say a wearable device. Neuromodulation parameters are estimated from the morphology of the ECG signal. Effect of exercise on cardiac parameters are simulated on open-source Kaggle dataset for healthy subjects, and hemodynamic parameters are evaluated.

The cardiac parameters estimated from the ECG signal associated with a subject, are integrated into the practical application of estimating blood pressure of the subject using the in-silico ECG driven cardiovascular model and with a meaningful combination of additional elements of generating the set of compliance functions and using the estimated parameters to sequentially activate the cardiac chambers of the in-silico ECG driven cardiovascular hemodynamic model.

As illustrated in <FIG>, the model simulated BP shows a correlation coefficient of <NUM> (for systolic BP), and <NUM> (for diastolic BP) with Kaggle data BP. The method and system of the present disclosure find application as a cardiac rehabilitation monitoring aid for both healthy and patients suffering from heart diseases, thereby providing cardiac care continuum.

Claim 1:
A processor implemented method (<NUM>) comprising:
estimating, by an in-silico cardiovascular hemodynamic model via one or more hardware processors, in each cardiac cycle of an electrocardiogram (ECG) signal, cardiac parameters based on morphology of the ECG signal associated with a subject, wherein the cardiac parameters include a continuous heart rate (HR) and a set of compliance parameters, and wherein estimating the set of compliance parameters is based on:
(i) a set of PQRST amplitudes; and
(ii) time-instances, ( <MAT>; j E m)
for a jth cardiac cycle (∀j ∈ m) of the ECG signal (<NUM>),
wherein the continuous HR is estimated, when the ECG signal is missing or noisy, and is represented as: <MAT>
where t is time considered over a complete cardiac cycle (T), hae (end) is the HR at a last instance of capturing of the ECG signal in the resting state and hae (<NUM>) is the HR at a first instance of the capturing of the ECG signal in the exercise state, wherein the subject is initially in the resting state and then performs exercise to reach the exercise state, w(t) = <MAT> is white-noise with zero-mean, variance of σ<NUM> = <NUM>, and τk = <NUM> sec defines a time constant, and wherein the σ<NUM>, and the τk are learnt empirically through the ECG signal using linear regression;
generating, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, a set of compliance functions using the estimated cardiac parameters (<NUM>);
sequentially activating, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, a plurality of cardiac chambers of the in-silico cardiovascular hemodynamic model, in a synchronized manner, using the generated set of compliance functions (<NUM>); and
estimating blood pressure of the subject, by the in-silico cardiovascular hemodynamic model via the one or more hardware processors, using the estimated cardiac parameters and the sequentially activated plurality of cardiac chambers to simulate the in-silico cardiovascular hemodynamic model in line with a human cardiovascular system of the subject, wherein the in-silico cardiovascular hemodynamic model is driven by the ECG signal associated with the subject (<NUM>).