Implementation of Neural Network and feature extraction to classify ECG signals

2018/02/17 by R Karthik, Karthik, R, Dhruv Tyagi +7
Computer Science · Medicine · Neuroscience · #ECG Monitoring and Analysis #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Fuzzy Logic and Control Systems #Neural and Evolutionary Computing (cs.NE)

paper · pdf · doi:10.48550/arxiv.1802.06288

openalex publication_date 2018/02/17 · openalex created_date 2018/03/06 · openalex updated_date 2026/07/28

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.

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