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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
ISBN13: 9781799811923|ISBN10: 1799811921|ISBN13 Softcover: 9781799811930|EISBN13: 9781799811947
DOI: 10.4018/978-1-7998-1192-3.ch001
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MLA

N., Raghu. "Arrhythmia Detection Based on Hybrid Features of T-Wave in Electrocardiogram." Deep Learning Techniques and Optimization Strategies in Big Data Analytics, edited by J. Joshua Thomas, et al., IGI Global, 2020, pp. 1-20. https://doi.org/10.4018/978-1-7998-1192-3.ch001

APA

N., R. (2020). Arrhythmia Detection Based on Hybrid Features of T-Wave in Electrocardiogram. In J. Thomas, P. Karagoz, B. Ahamed, & P. Vasant (Eds.), Deep Learning Techniques and Optimization Strategies in Big Data Analytics (pp. 1-20). IGI Global. https://doi.org/10.4018/978-1-7998-1192-3.ch001

Chicago

N., Raghu. "Arrhythmia Detection Based on Hybrid Features of T-Wave in Electrocardiogram." In Deep Learning Techniques and Optimization Strategies in Big Data Analytics, edited by J. Joshua Thomas, et al., 1-20. Hershey, PA: IGI Global, 2020. https://doi.org/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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