ECG Feature Extraction Techniques - A Survey Approach

TL;DR

This survey reviews various ECG feature extraction techniques, including machine learning and signal analysis methods, comparing their advantages and limitations to aid in cardiac disease diagnosis.

Contribution

It provides a comprehensive overview and comparative analysis of existing ECG feature extraction methods, highlighting their strengths and weaknesses.

Findings

Fuzzy Logic, ANN, GA, SVM are commonly used techniques.

Different methods have varying accuracy and computational efficiency.

The survey identifies gaps and future directions in ECG feature extraction.

Abstract

ECG Feature Extraction plays a significant role in diagnosing most of the cardiac diseases. One cardiac cycle in an ECG signal consists of the P-QRS-T waves. This feature extraction scheme determines the amplitudes and intervals in the ECG signal for subsequent analysis. The amplitudes and intervals value of P-QRS-T segment determines the functioning of heart of every human. Recently, numerous research and techniques have been developed for analyzing the ECG signal. The proposed schemes were mostly based on Fuzzy Logic Methods, Artificial Neural Networks (ANN), Genetic Algorithm (GA), Support Vector Machines (SVM), and other Signal Analysis techniques. All these techniques and algorithms have their advantages and limitations. This proposed paper discusses various techniques and transformations proposed earlier in literature for extracting feature from an ECG signal. In addition this paper also provides a comparative study of various methods proposed by researchers in extracting the feature from ECG signal.