IoT 환경에서 최적 R파 검출 및 최소 특징점 추출을 통한 향상된 PVC 분류방법
Optimal R Wave Detection and Advanced PVC Classification Method through Extracting Minimal Feature in IoT Environments
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.
Abstract
Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.
참고문헌 (10)
- M. J. Rooijakkers, "Feasibility Study of a New Method for Low-Complexity Fetal Movement Detection From Abdominal ECG Recordings", IEEE Journal of Biomedical and Health Informatics, 20(5), pp.1361~1368 (2016)
- Oster J, "Semi-supervised ECG ventricular beat classification with novelty detection based on switching Kalman filters", IEEE Transactions on Biomedical Engineering, 62(9), pp.2125~2134 (2015)
- 조익성, "헬스케어 환경에서 복잡도를 고려한 R파 검출과 이진 부호화 기반의 부정맥 분류방법", (사)디지털산업정보학회 논문지, 12(4), pp.33~40 (2016), (사)디지털산업정보학회[DOI]
- S. Pal, "Empirical mode decomposition based ECG enhancement and QRS detection", Computers in Biology and Medicine, 42(1), pp.83~92 (2012)
- Y.-L. Zheng, "Unobtrusive sensing and wearable devices for health informatics", IEEE Transactions on Biomedical Engineering, 61(5), pp.1538~1554 (2014)
- P. Li, "High-Performance Personalized Heartbeat Classification Model for Long-Term ECG Signal", IEEE Transactions on Biomedical Engineering, 64(1), pp.78~86 (2017)
- H. Xia, "Cloud-ECG for real time ECG monitoring and analysis", Journal of Computer Methods and Programs in Biomedicine, 110(3), pp.253~259 (2013)
- C. J. Deepu, "A 3-Lead ECG-on-Chip with QRS Detection and Lossless Compression for Wireless Sensors", IEEE Transactions on Circuits and Systems II: Express Briefs, 63(12), pp.1151~1155 (2016)
- 조익성, "조기심실수축 분류를 위한 위상 변이 추적 기반의 QRS 특징점 검출", 한국정보통신학회논문지, 20(2), pp.427~436 (2016), 한국정보통신학회[DOI]
- 조익성, "심전도신호 샘플링 주파수에 따른 R파 검출 최적 문턱치 설정", 한국정보통신학회논문지, 21(7), pp.1420~1428 (2017), 한국정보통신학회[DOI]