ECG Based Heart Arrhythmia Detection Using Wavelet Coherence and Bat Algorithm

被引：16

作者：

Kora P. ^{[1
]}

Sri Rama Krishna K. ^{[2
]}

机构：

[1] Department of E.C.E., Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana

[2] Department of E.C.E., V. R. Siddhartha Engineering College, Vijayawada, Andhra Pradesh

来源：

Sensing and Imaging | 2016年 / 17卷 / 01期

关键词：

Atrial fibrillation; Bat algorithm; Wavelet coherence;

D O I：

10.1007/s11220-016-0136-5

中图分类号：

学科分类号：

摘要：

Atrial fibrillation (AF) is a type of heart abnormality, during the AF electrical discharges in the atrium are rapid, results in abnormal 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 calculates 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 Bat algorithm. The Levenberg Marquardt neural network classifier is used to classify the optimized features. The performance of the classifier can be improved with the optimized features. © 2016, Springer Science+Business Media New York.

引用

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