Classification of ECG Arrythmia beats with Artificial Neural Networks

Abstract

In this study, Electrocardiographic(ECG) Arrythmias were classified by using Artificial Neural Networks (ANN). During the training process of ANN, the ECG recordings from MIT BIH Arrythmia database are used as a reference. 24 recordings out of 48 30 minutes recordings in this database were used for data extraction. In order to have more realistic data, the extractons were made from different recordings, and, the typical ECG signals with acceptable amount of noise were included. The arrhythmia samples that are extracted from the database were prepreprocessed to create input sets to train ANNs. The Fourier Transforms of a predefined window of signals were taken as a feature extraction method. As a result of this study, 5 types of ECG signals (Ventricular Tachicardy, Left Bundle Branch Block, Right Bundle Branch Block, Atrial Fibrillation, Normal ECG) were labeled with 82% accuracy.