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Heart Arhythmia Detection using Wavelet Coherence and Firefly Algorithm

by Padmavathi Kora, Ch. Usha Kumari, K. Meenakshi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 27
Year of Publication: 2018
Authors: Padmavathi Kora, Ch. Usha Kumari, K. Meenakshi
10.5120/ijca2018916539

Padmavathi Kora, Ch. Usha Kumari, K. Meenakshi . Heart Arhythmia Detection using Wavelet Coherence and Firefly Algorithm. International Journal of Computer Applications. 179, 27 ( Mar 2018), 1-8. DOI=10.5120/ijca2018916539

@article{ 10.5120/ijca2018916539,
author = { Padmavathi Kora, Ch. Usha Kumari, K. Meenakshi },
title = { Heart Arhythmia Detection using Wavelet Coherence and Firefly Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2018 },
volume = { 179 },
number = { 27 },
month = { Mar },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number27/29125-2018916539/ },
doi = { 10.5120/ijca2018916539 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:56:36.424490+05:30
%A Padmavathi Kora
%A Ch. Usha Kumari
%A K. Meenakshi
%T Heart Arhythmia Detection using Wavelet Coherence and Firefly Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 27
%P 1-8
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Atrial Fibrillation (AF) is a common type of heart abnormality. During the AF electrical discharges in the atrium are rapid that results in irregular heart beat. The morphology of ECG changes due to the abnormalities in the heart. This paper consists of three major steps for the detection of heart diseases: signal pre-processing, feature extraction and classification. Feature extraction is the key process in detecting the heart abnormality. Most of the ECG detection systems depend on the time domain features for cardiac signal classification. In this paper we proposed a Wavelet Coherence (WTC) technique for ECG signal analysis. The WTC measures the similarity between two waveforms in frequency domain. Parameters extracted from WTC function is used as the features of the ECG signal. These features are optimized using Firefly algorithm (FFA). The optimized features from the FFA are given as the input to the Levenberg Marquardt Neural Network (LM NN) classifier. From the literature it is observed that the performance of the classifier is improved with the help of the optimized (reduced) features.

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Index Terms

Computer Science
Information Sciences

Keywords

ECG Atrial Fibrillation Wavelet Coherence Firefly algorithm.

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