Implementation of Neural Network and feature extraction to classify ECG signals

TL;DR

This paper combines the Pan Tompkins algorithm for feature extraction from ECG signals with neural network classifiers to detect and differentiate four cardiac diseases from normal heartbeats.

Contribution

It introduces an efficient ECG feature extraction method integrated with neural networks for improved cardiac disease classification.

Findings

Effective detection of four cardiac diseases

High accuracy in differentiating normal and abnormal beats

Integration of feature extraction with neural networks enhances classification

Abstract

This paper presents a suitable and efficient implementation of a feature extraction algorithm (Pan Tompkins algorithm) on electrocardiography (ECG) signals, for detection and classification of four cardiac diseases: Sleep Apnea, Arrhythmia, Supraventricular Arrhythmia and Long Term Atrial Fibrillation (AF) and differentiating them from the normal heart beat by using pan Tompkins RR detection followed by feature extraction for classification purpose .The paper also presents a new approach towards signal classification using the existing neural networks classifiers.