Arrhythmia Detection Based on Hybrid Features of T-Wave in Electrocardiogram

Arrhythmia Detection Based on Hybrid Features of T-Wave in Electrocardiogram

Raghu N.
Copyright: © 2020 |Pages: 20
DOI: 10.4018/978-1-7998-1192-3.ch001
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Abstract

An electrocardiogram (ECG) is used as one of the important diagnostic tools for the detection of the health of a heart. An automatic heart abnormality identification methods sense numerous abnormalities or arrhythmia and decrease the physician's pressure as well as share their workload. In ECG analysis, the main focus is to enhance degree of accuracy and include a number of heart diseases that can be classified. In this chapter, arrhythmia classification is proposed using hybrid features of T-wave in ECG. The classification system consists of majorly three phases, windowing technique, feature extraction, and classification. This classifier categorizes the normal and abnormal signals efficiently. The experimental analysis showed that the hybrid features arrhythmia classification performance of accuracy approximately 98.3%, specificity 98.0%, and sensitivity 98.6% using MIT-BIH database.
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Literature Survey

A. Daamouche, L. Hamami, N. Alajlan, and F. Melgani (2012) presented a wavelet optimization strategy depends on the mixture of the poly phase representation of wavelets and PSO. This strategy finds the wavelets that indicate the beats of discrimination capability calculated through an empirical measure of the classifier efficiency. The SVM classifier illuminates the accuracy and stability of the proposed method and poly phase permits the wavelet filter bank from angular parameter. The wavelet method for ECG signal improves the classification accuracy but, this proposed technique not suitable for all datasets.

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