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http://dx.doi.org/10.17662/ksdim.2021.17.1.007

R Wave Detection and Advanced Arrhythmia Classification Method through QRS Pattern Considering Complexity in Smart Healthcare Environments  

Cho, Iksung (대구대학교 자유전공학부)
Publication Information
Journal of Korea Society of Digital Industry and Information Management / v.17, no.1, 2021 , pp. 7-14 More about this Journal
Abstract
With the increased attention about healthcare and management of heart diseases, smart healthcare services and related devices have been actively developed recently. R wave is the largest representative signal among ECG signals. R wave detection is very important because it detects QRS pattern and classifies arrhythmia. Several R wave detection algorithms have been proposed with different features, but the remaining problem is their implementation in low-cost portable platforms for real-time applications. In this paper, we propose R wave detection based on optimal threshold and arrhythmia classification through QRS pattern considering complexity in smart healthcare environments. For this purpose, we detected R wave from noise-free ECG signal through the preprocessing method. Also, we classify premature ventricular contraction arrhythmia in realtime through QRS pattern. The performance of R wave detection and premature ventricular contraction arrhythmia classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 premature ventricular contraction. The achieved scores indicate the average of 98.72% in R wave detection and the rate of 94.28% in PVC classification.
Keywords
R wave; QRS; Arrhythmia; Premature Ventricular Contraction; MIT-BIH Database; Complexity; Smart Healthcare;
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