In this paper, we present a methodology for classifying normal, atrial fibrillation (AF), non-AF related other abnormal heart rhythms and noisy recordings by analysing single lead ECG signal of short duration.In two layer binary cascaded approach proposed in our methodology, an unlabelled recording is initially classified into one the intermediate classes ('normal+others' 'AF+noisy') at first before actual classification second layer.The Physionet Challenge 2017 dataset containing more than...

Objective: Atrial fibrillation (AF) and other types of abnormal heart rhythm are related to multiple fatal cardiovascular diseases that affect the quality human life. Hence development an automated robust method can reliably detect AF, in addition non-sinus sinus rhythms, would be a valuable medicine. The present study focuses on developing algorithm for classification short, single-lead electrocardiogram (ECG) recordings into normal, rhythms noisy classes. Approach: proposed framework...

Valvular heart diseases are a prevalent cause of cardiovascular morbidity and mortality worldwide, affecting wide spectrum the population. In-silico modeling system has recently gained recognition as useful tool in research clinical applications. Here, we present an in-silico cardiac computational model to analyze effect severity valvular disease on general hemodynamic parameters. We propose multimodal multiscale simulate understand progression associated with mitral valve. The developed...

Electrocardiogram (ECG) is one of the fundamental markers to detect different cardiovascular diseases (CVDs). Owing widespread availability ECG sensors (single lead) as well smartwatches with recording capability, classification using wearable devices CVDs has become a basic requirement for smart healthcare ecosystem. In this paper, we propose novel method model compression robust detection capability from signals such that sophisticated and effective baseline deep neural network can be...

Deep Learning (DL) performs well in Cardiovascular Disease (CVD) classification using 12-lead Electrocardiogram (ECG). However, explainable artificial intelligence (xAI) CVD classification, still remains largely qualitative. In this paper, we introduce a Region of Interest (ROI) based quantifiable xAI (qxAI), to compare different techniques. Then, add specific post-processing steps, increase the explanation performance. Furthermore, proposed qxAI enables selection an optimal DL model, within...

Atrial Fibrillation (AF) is a type of abnormal heart rhythm which may lead to stroke or cardiac arrest. In spite numerous research works, developing an automatic mechanism for accurate detection AF remains popular yet unsolved problem. this paper, we propose deep neural network architecture classification using single-lead Electrocardiogram (ECG) signals short duration. We define novel Recurrent Neural Network (RNN) structure, comprising two Long-Short Term Memory (LSTM) networks temporal...

Worldwide revenue of pharmaceutical market is more than 1200 billion USD [1] and that counterfeit medicines around 200 [2][3]. Counterfeit can be detected by technical experts using visual inspection or through sophisticated lab relevant methods. However, such methods require time, sample preparation expertise with setup. These are not feasible scalable to used in the field general public. The objective our research work was detect simpler faster method hyperspectral sensing. In this...

Atrial Fibrillation (AF) is a kind of arrhythmia, which major morbidity factor, and AF can lead to stroke, heart failure other cardiovascular complications. Electrocardiogram (ECG) the basic marker test condition it effectively detect condition. Single ECG has practical advantage for being small form factor easy deploy. With sophistication current deep learning (DL) models, researchers have been able construct cardiologist-level models different arrhythmias including detection from single...

When judging the quality of a computational system for pathological screening task, several factors seem to be important, like sensitivity, specificity, accuracy, etc. With machine learning based approaches showing promise in multi-label paradigm, they are being widely adopted diagnostics and digital therapeutics. Metrics usually borrowed from literature, current consensus is report results on diverse set metrics. It infeasible compare efficacy systems which have been evaluated different...

Cardiopulmonary disease prognosis can achieve therapeutic edge if the disorders be detected and attended to at an early stage. This work proposes 'Cardiopulmonary Care Platform (C2P)' which in its current initial phase, targets subjects stage of Functional Capacity II as per NYHA staging system by detecting physiological fatigue, signs dispnea palpitation, using a smartwatch after subject has undergone spell physically intensive activity. A novel computationally efficient solution is devised...

Deep learning techniques are being used for heart rhythm classification from ECG waveforms. Large networks using end-to-end such as convolutional neural not easily interpretable by end-users doctors. This is because most of the state art explainability focus on explanations data-scientists who have technical knowledge. However, systems normally doctors familiar with details machine learning. Therefore, to address this gap, we propose a framework that provides explanation model in language...

In the paradigm of remote patient monitoring, clinical significance, robustness and repeatability measurements are key aspects. Such using sensors or wearables, implantable proxemics devices often termed "Digital Biomarkers" as they surrogate markers for patient's health condition. We have invested in creating digital biomarkers platform which connects patients & physicians by enabling monitoring augmenting reports physical examination data with insights derived from continuous these...

Wearable cardioverter defibrillator (WCD) is a life saving, wearable, noninvasive therapeutic device that prevents fatal ventricular arrhythmic propagation leads to sudden cardiac death (SCD). WCD are frequently prescribed patients deemed be at high risk but the underlying pathology potentially reversible or those who awaiting an implantable cardioverter-defibrillator. programmed detect appropriate events and generate energy shock capable of depolarizing myocardium thus re-initiating sinus...

We often observe long-tailed distribution in real-world classification problems and consequently, maintaining balanced predictive performance across all the classes is a research challenge. Further, we find, particularly time series tasks like prediction of clinical diseases from physiological signals Electrocardiogram (ECG), existence critically important rare cost low sensitivity towards such yet critical are extremely high not only with higher treatment expenses, but also chances...

According to World Health Organization (WHO) cardiovascular diseases (CVDs) are the number one cause of global deaths annually. More than 17 million people die each year from CVDs. To diagnose CVDs some clinical tests required which invasive and non-invasive both also expensive. Hence, there is a need for cheap, reliable sensing that easily available can be used by layman. We presenting method harvest energy touchpad bio sensing. Today, almost everyone has smartphone its capacitive...

In this paper, we propose a cardiovascular digital twin platform to simulate the effect of exercise on various cardiac parameters medical importance. The model incorporates real-time ECG signal from body-worn sensors estimate level and compute variables like left ventricular dynamics, output, ejection fraction, mean arterial pressure, etc., an individual while performing exercises. novel contribution work is determine compliances morphology single-lead Systemic resistance estimation...

In this paper, we present a cardiac computational framework aimed at simulating the effects of ischemia on potentials and hemodynamics. Proposed model uses an image based pipeline for modeling analysis ischemic condition in-silico. We compute epicardial potential as well body surface (BSP) acute conditions data from animal while varying both local coronary supply global metabolic demand. Single lead ECG equivalent signal processed computed BSP is used to drive lumped hemodynamic derive left...

Valvular heart disease (VHD) is an important cause of cardiovascular morbidity and mortality worldwide, affecting the aging population also younger in case rheumatoid related valve disorders. In this paper, we present a simulation platform to study do predictive analysis on valvular disease, Mitral stenosis (MS) particular propose control approach correct hemodynamic imbalances during severe MS conditions. Our developed hemodynamics model helps create `what if' conditions variation different...

In this paper, we present a computational fluid dynamic (CFD) analysis to capture the effect of physical stress and stenosis severity in coronary arteries leading changes supply demand oxygen equilibrium. We propose coupled Od-3d vessel model predict variation flow dynamics as well arterial system, modeled using an in-silico replicating cardiovascular hemodynamics. CFD simulation were solved subject specific CT scan for pressure along with metrics related wall shear stress. Simulations...

Automated detection of cardiovascular diseases (CVDs) from Electrocardiogram (ECG) recordings is a problem immense practical interest and it associated with considerable research challenges. In this paper, we develop an ECG classification model that capable satisfying clinical requirement automated CVD screening solution, where the medical domain principle to minimize false negative rates decisive diagnosis or improve sensitivity critical classes. work, attempt solve unique challenge propose...

This paper investigates a subject-specific lumped parameter cardiovascular model for estimating Cardiac Output (CO) using the radial Arterial Blood Pressure (ABP) waveform. The integrates simplified of left ventricle along with linear third order arterial tree and generates reasonably accurate ABP waveforms Dicrotic Notch (DN). Non-linear least square optimization technique is used to obtain uncalibrated estimates parameters. Thermodilution CO measurements have been evaluate estimation...

The enormous demand for annotated data brought forth by deep learning techniques has been accompanied the problem of annotation noise. Although this issue widely discussed in machine literature, it relatively unexplored context "multi-label classification" (MLC) tasks which feature more complicated kinds Additionally, when domain question certain logical constraints, noisy annotations often exacerbate their violations, making such a system unacceptable to an expert. This paper studies effect...