Arrhythmia classification with single beat ECG evaluation and support vector machine

Tribhuvanam, S and Nagaraja, HC and Naidu, VPS (2019) Arrhythmia classification with single beat ECG evaluation and support vector machine. International Journal of Innovative Technology and Exploring Engineering, 8 (12). pp. 1-7. ISSN 22783075

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Abnormal electrical activity of the human heart indicates cardiac dysfunction. The Electrocardiogram (ECG) is one of the non-invasive diagnostic techniques to detect cardiac abnormalities. Irregularity and non-stationarity in the ECG signal impose difficulties to clinicians for accurate diagnosis of heart diseases only by visual inspection. Automatic recognition of abnormal ECG beats aids in early detection of heart diseases. This paper explores the ECG single beat analysis to identify the cardiac abnormality. In this work, seven classes of arrhythmia are considered as recommended by AAMI(Association for the Advancement of Medical Instrumentation) standard. Beat feature database is generated from 44 recordings of the MIT-BIH arrhythmia database to support the arrhythmia classification. Classification is implemented with Multiclass Support Vector Machine (SVM) for non-linearly separable data effectively. Classification accuracy up to 93% is achieved for the selected input feature sets. This work assesses the suitability of the ECG input features for multi-class classification of arrhythmia.

Item Type: Article
Subjects: ENGINEERING > Fluid Mechanics and Thermodynamics
ENGINEERING > Mechanical Engineering
LIFE SCIENCES > Behavioral Sciences
LIFE SCIENCES > Man/System Technology and Life Support
Depositing User: Mrs SK Pratibha
Date Deposited: 12 Nov 2020 14:02
Last Modified: 12 Nov 2020 14:02

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