Abstract: It is important to monitor the cardiac condition of an individual outside the clinic, using wearable physiological sensors. However, existing methods for calculating the cardiac risk score of an individual are primarily based on static information like individual's metadata, lifestyle, family history, clinical assessment, etc. but do not consider the cardiac state in a daily living scenario using wearable-based measurements. Embodiments herein provide a method and a system for determining post-exercise cardiac score in a recovery period using personalized cardiac model. A clinical decision support system (CDSS) is disclosed to predict cardiac recovery score of a subject in post-exercise conditions. The system employs a hybrid approach using a computational cardiac model and wearable data. Further, several personalized cardiac parameters are simulated using a cardiovascular simulation (CVS) platform.

Abstract: The present invention relates to a method and system for determining progression of atrial fibrillation (AF) based on hemodynamic metrics. In conventional CFD models, effect of the AF on a cardiovascular system is not modeled and evaluation of associated hemodynamic metrics and its effect on a Left Atrium (LA) dynamics is not considered. The method and system for determining progression of the AF based on the hemodynamic metrics, analyzes the effect of the AF on cardiovascular parameters of the LA and a left Ventricle (LV), for AF variations. A 3D-CFD model is modelled from a plurality of scan images of a heart of a subject and the AF variations are incorporated in a zero-dimensional (OD) lumped cardiovascular hemodynamic model along with a novel rhythm generator that are used for extracting a plurality of LA hemodynamic metrics of wall shear stress (WSS) that are possible indicators for progression of the AF.

Abstract: This disclosure relates generally to in-silico modeling of hemodynamic patterns of physiologic blood flow. 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. The present disclosure enables estimating blood pressure of a subject by estimating cardiac parameters based on the morphology of ECG signal associated with the subject and hence activation delays in cardiac chambers of the in-silico model is reproduced purposefully. In accordance with the present disclosure, the blood pressure of the subject can be estimated using only the ECG signal even if the signal is missed for some time instance(s) or is noisy.

Abstract: This disclosure relates generally to method and system for determining myocardial ischemia severity based on hemodynamic parameters estimation. Many patients suffer from myocardial ischemia due to narrowing of coronary artery resulting poor oxygen supply in cardiac muscles. The method includes receiving Electrophysiology (EP) signal from a simulated heart surface model to generate a single lead ECG template. The method further estimates hemodynamic parameters using a hemodynamic module based on the single lead ECG template and then estimates cardiac pressure-volume loop variables. The myocardial ischemia severity of the heart surface model is determined which includes one of moderate ischemia, severe ischemia and silent ischemia. Here, the cardiac source module is coupled with the hemodynamic module to determine cardiac transmembrane potential (TMP) of the heart surface model through contractility function.

Abstract: State of the art systems and methods attempting to generate synthetic biosignals such as PPG generate patient specific PPG signatures and do not correlate with pathophysiological changes. Embodiments herein provide a method and system for generating synthetic time domain signals to build a classifier. The synthetic signals are generated using statistical explosion. Initially, a parent dataset of actual sample data of class and non-class subjects is identified, and statistical features are extracted. Kernel density estimate (KDE) is used to vary the feature distribution and create multiple data template from a single parent signal. PPG signal is again reconstructed from the distribution pattern using non-parametric techniques. The generated synthetic data set is used to build the two stage cascaded classifier to classify CAD and Non CAD, wherein the classifier design enables reducing bias towards any class.

Abstract: This disclosure provides a simulation platform to study and perform predictive analysis on valvular heart disease, Mitral stenosis (MS) and provides a control approach to correct hemodynamic imbalances during MS conditions. Conventional approaches of valve repair or replacement are often associated with risk of thromboembolism, need for anticoagulation, prosthetic endocarditis, and impaired left ventricle function. The cardiovascular hemodynamics model of the present disclosure helps to create ‘what if’ conditions to study variations in different hemodynamic parameters like blood flow, aortic and ventricular pressure, etc. during normal and pathological conditions. An adaptive control system in conjunction with the hemodynamic cardiovascular system (CVS) is provided to handle hemodynamic disbalance during moderate to severe MS conditions.

Abstract: This disclosure relates generally to a method and system for online handwritten signature verification providing a simpler low cost system. The method comprises extracting signature data for the subject from a sensor array for the predefined time window at regular predefined time instants. Further, differentiating the matrix row wise and column wise to generate a row difference matrix and a column difference matrix. Further, determining an idle signature time fraction for the extracted signature data of the subject being monitored from the column difference matrix. Further, determining a plurality of signature parameters based on the row difference matrix and the column difference matrix.

Abstract: The disclosure relates to digital twin of cardiovascular system called as cardiovascular model to generate synthetic Photoplethysmogram (PPG) signal pertaining to disease conditions. The conventional methods are stochastic model capable of generating statistically equivalent PPG signals by utilizing shape parameterization and a nonstationary model of PPG signal time evolution. But these technique generates only patient specific PPG signatures and do not correlate with pathophysiological changes. Further, these techniques like most synthetic data generation techniques lack interpretability. The cardiovascular model of the present disclosure is configured to generate the plurality of synthetic PPG signals corresponding to the plurality of disease conditions. The plurality of synthetic PPG signals can be used to tune Machine Learning algorithms. Further, the plurality of synthetic PPG signals can be utilized to understand, analyze and classify cardiovascular disease progression.

Abstract: This disclosure relates to monitoring of machines having one or more vibrating objects. Conventional systems that address the technical problem of detecting frequency of vibrating objects are expensive, require manual intervention, sometimes depend on prior knowledge of location of faults or involve high convergence time. Systems and methods of the present disclosure provide a cost-effective and fully automated solution that employ multiple strobe sources along with a low cost camera. Besides being cost-effective and automated, the solution also reduces convergence time significantly. Employing multiple strobe sources enables generating multiple strobing frequencies in a single iteration. The strobing frequency is a configured to be a multiple of a camera frame rate selected from a set of camera frame rates having mutually prime elements to ensure faster convergence compared to the art.

Abstract: This disclosure relates generally to methods and systems for unobtrusive and automated detection of frequencies of spatially located distinct parts of a machine. Location of vibration and detection of vibration frequency of each vibrating part in a machine is critical for routine monitoring and fault detection in the machine. Current solutions use either high frames per second (fps) industrial grade camera or stroboscopes tuned at one particular frequency. Manual stroboscopes require manual intervention for objects moving at different speeds with high convergence time. Point-lasers need prior knowledge of exact location of faults. Also Point-by-point scanning of a large machine body is time consuming. In the present disclosure, a movement detector such as RADAR enables detecting all vibration frequencies that also serve to reduce the search space of a stroboscope configured to start strobing at each detected vibration frequency to enable mapping of each vibration frequency to a corresponding vibrating part.

Abstract: An acoustic array system for anomaly detection is provided. The acoustic array system (100) performs a scan (or a progressive scan of frequencies) of a given volume by transmitting one or more signals, and receives one or more reflected signals from objects within the volume. The reflected signals are then amplified and converted to a set of digital signals. Features of the set of digital signals are extracted both in time and frequency domains. The acoustic array system (100) further performs a comparison of these set of digital extracted features with the reflected signals via machine learning techniques. Based on the comparison, the acoustic array system detects one or more anomalies.