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

• Published in: (사)디지털산업정보학회 논문지, 17(1) pp. 7 - 14
• Main Author: 조익성
• Format: Journal Article
• Language: English
• Published: (사)디지털산업정보학회 01.03.2021
• Subjects: 컴퓨터학
• ISSN: 1738-6667; 2713-9018

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. KCI Citation Count: 0