Akademik Veri Yönetim Sistemi
Balkan Journal of Electrical and Computer Engineering, cilt.9, sa.1, ss.53-58, 2021 (Hakemli Dergi)
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.