Journal of Korea Society of Digital Industry and Information Management (디지털산업정보학회논문지)

Korea Society of Digital Industry and Information Management (디지털산업정보학회)
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R Wave Detection and Advanced Arrhythmia Classification Method through QRS Pattern Considering Complexity in Smart Healthcare Environments

스마트 헬스케어 환경에서 복잡도를 고려한 R파 검출 및 QRS 패턴을 통한 향상된 부정맥 분류 방법

  • 조익성 (대구대학교 자유전공학부)
  • Received : 2021.02.10
  • Accepted : 2021.03.17
  • Published : 2021.03.30
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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.

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References

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