Deep Learning Algorithms for Efficient Analysis of ECG Signals to Detect Heart Disorders

Abstract

Electrocardiography (ECG) has been a reliable method for monitoring the proper functioning of the cardiovascular system for decades. Recently, there has been a lot of research focusing on accurately analyzing the heart condition through ECG. In recent days, numerous attempts are being made to analyze these signals using deep learning algorithms, including the implementation of artificial neural networks like convolutional neural networks, recurrent neural networks, and the like. In this context, this chapter intends to present some important techniques for classifying heartbeats based on deep neural networks with 1D CNN. Five ECG signals (N, S, V, F, and Q) standardization are based on the AAMI EC57 standard. The primary focus of this chapter is to discuss the techniques to classify ECG signals in those classes with promising accuracy and draw a clear picture of the current state-of-the-art in this sphere of study.

Relative Learning Rate Adaptation on Loss Feedback

“…The emergence of deep learning has introduced the world to a new era of solving stochastic decision problems. Deep learning algorithms have been adopted in various domains that include self-driving cars in the automotive industry [1], computer vision in agriculture [2], manufacturing autonomous Unmanned Aerial Vehicle (UAVs) [3] and its auxiliary applications [4], image and signal processing in medical science [5,6], and so on. However, these algorithms require massive computational resources, time, and enormous data for their training process.…”