K-Means Clustering Algorithm Based Arrhythmic Heart Beat Detection in ECG Signal

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Yakut Ö., Bolat E., Efe H.

Balkan Journal of Electrical and Computer Engineering, vol.9, no.1, pp.53-58, 2021 (Peer-Reviewed Journal)


Disorders in the functions of the heart cause heart diseases or arrhythmias in the cardiovascular system. Diagnosis of cardiac arrhythmias is made using the Electrocardiogram which measures and records electrophysiological signals. In this study, a three-class, K-means clustering-based arrhythmia detection method was proposed, distinguishing the cardiac arrhythmia type Right Bundle Branch Block and Left Bundle Branch Block from normal heartbeats. Data from the MIT-BIH Arrhythmia Database were analyzed for clustering-based arrhythmia analysis. Feature Set 1 (FS1) was created by extracting the features from the Electrocardiogram signal with the help of QRS morphology, Heart Rate Variability and statistical metrics. The RELIEF feature selection algorithm was used for dimension reduction of the obtained features and Feature Set 2 (FS2) was obtained by determining the most appropriate features in FS1. Overall performance results for FS1 were 99.18% accuracy, 98.78% sensitivity, and 99.39% specificity, while overall performance results for FS2 were 95.37% accuracy, 92.99% sensitivity and 96.54% specificity. In this study, the computational cost was decreased by reducing the processing complexity and load, utilizing the reduced feature data set of FS2 and an arrhythmia detection method having a satisfactory level of high performance was proposed.