Abstract
Cardiac Arrhythmias shows a condition of abnor-mal electrical activity in the heart which is a threat to humans. This paper presents a method to analyze electrocardiogram (ECG) signal, extract the fea-tures, for the classification of heart beats according to different arrhythmias. Data were obtained from 40 records of the MIT-BIH arrhythmia database (only one lead). Cardiac arrhythmias which are found are Tachycardia, Bradycardia, Supraventricular Tachycardia, Incomplete Bundle Branch Block, Bundle Branch Block, Ventricular Tachycardia. A learning dataset for the neural network was obtained from a twenty records set which were manually classified using MIT-BIH Arrhythmia Database Directory and docu- mentation, taking advantage of the professional experience of a cardiologist. Fast Fourier transforms are used to identify the peaks in the ECG signal and then Neural Networks are applied to identify the diseases. Levenberg Marquardt Back-Propagation algorithm is used to train the network. The results obtained have better efficiency then the previously proposed methods.
Highlights
Electrocardiogram contains a wealth of diagnostic information routinely used to guide clinical decision making
This paper presents a method to analyze electrocardiogram (ECG) signal, extract the features, for the classification of heart beats according to different arrhythmias
Fast Fourier transforms are used to identify the peaks in the ECG signal and Neural Networks are applied to identify the diseases
Summary
Electrocardiogram contains a wealth of diagnostic information routinely used to guide clinical decision making. With the features present in ECG Signal various Cardiac Arrythmias can be predicted. Within the last decade many new approaches to feature extraction have been proposed, for example, algorithms from the field of artificial neural networks [2,3,4,5], genetic algorithms [6], wavelet transforms [7], filter as well as heuristic methods mostly based on nonlinear transforms. Beyond feature extraction and deflection identification, many papers have been published in related fields. Conventional approach to predict these diseases is to analyze the ECG signal by the doctor manually.
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