Tan Ru San

TL;DR: The proposed Computer-Aided Diagnosis (CAD) system can be used for long-term monitoring of the human heart and is the first to incorporate deep learning for AF beat detection.

TL;DR: In this study, ECG signals are subjected to DCT, DWT and EMD to obtain respective coefficients, which are reduced using Locality Preserving Projection (LPP) data reduction method, and ranked using F-value to achieve the best classification performance.

TL;DR: The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification.

TL;DR: This work highlights the development of an ECG-based CAD diagnostic system that employs deep learning algorithms to automatically detect Congestive heart failure, and reviews existing CAD for automatic CHF diagnosis.

TL;DR: In this article, an automated system was developed for the automated categorization of electrocardiogram signals into normal, CAD, myocardial infarction (MI) and congestive heart failure (CHF) classes using convolutional neural network (CNN) and unique GaborCNN models.