An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

Abstract

Potentially lethal heart abnormalities can be detected/spotted with recent evolution in continuous, long-term cardiac health monitoring using wearable sensors. However, the huge data accumulated presents a challenge in terms of storage, knowledge extraction and computing time. Moreover, manual examination of long-term ECG recordings presents various problems like huge time and work demand, inter-observer variations and difficulty classifying complex non-linear single-lead ECG signal. To address these problems, we propose an automatic heartbeat classification system that uses the optimized minimum number of features using ECG time-series amplitude directly as input, without feature extraction and provides a primary classification and diagnosis for 1 normal and 14 types of arrhythmic heartbeats. Multi-objective particle swarm optimization (MOPSO) is used to achieve the best feature fitness. A novel fitness function is designed to be the sum of macro F1 loss and normalized dimension, with the optimization objective calculated as the minimum of the fitness function. Multi-layer perceptron (MLP), k-nearest neighbor, support vector machine, random forest and extra decision tree classifiers are trained using the selected features. For the targeted 15-class classification problem, MOPSO-optimized features with MLP consistently performed best with significantly reduced number of features. The proposed method proves to be an efficient and effective arrhythmia identification system for continuous, long-term cardiac health monitoring using single-lead ECG signal.

An RNN-Bi LSTM Based Multi Decision GAN Approach for the Recognition of Cardiovascular Disease (CVD) From Heart Beat Sound: A Feature Optimization Process

“…These components are characterized by time-domain features like amplitude, duration, interval, and segment [3], as illustrated in Figure 1. The sequential depolarization of the atria and ventricles generates distinct waveforms [4]. Abnormalities in the ventricles or atria can cause irregular ECG waveforms, enabling the diagnosis of various cardiac arrhythmias such as premature ventricular contraction (V), atrial premature beat (A), and right bundle branch blocks (R).…”

“…S2, or dub, is generated in diastole as the aortic and pulmonic valves are closing. These include another softer sound known as the S3, S4, murmur due to arterial turbulence of blood flow, ejection click (EC) during systole, opening snap/snaps (OS) in diastole and mid systolic snicks [4]. One of the early means of diagnosis for diseases of the valvular heart includes cardiac auscultation, which entails listening to the heartbeat using a stethoscope.…”