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

Korea Society of Digital Industry and Information Management (디지털산업정보학회)
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Optimal R Wave Detection and Advanced PVC Classification Method through Extracting Minimal Feature in IoT Environments

IoT 환경에서 최적 R파 검출 및 최소 특징점 추출을 통한 향상된 PVC 분류방법

  • 조익성 (경운대학교 항공정보통신과) ;
  • 우동식 (경운대학교 항공정보통신과)
  • Received : 2017.11.21
  • Accepted : 2017.12.20
  • Published : 2017.12.30
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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.

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References

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