Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Automatic Noise Removal and Peak Detection Algorithm for ECG Measured from Capacitively Coupled Electrodes Included within a Cloth Mattress Pad

침대 패드 형태의 용량성 전극에서 측정된 심전도 신호를 처리하기 위한 자동 잡음 제거 및 피크 검출 알고리즘

  • Lee, Won Kyu (Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University) ;
  • Lee, Hong Ji (Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University) ;
  • Yoon, Hee Nam (Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University) ;
  • Chung, Gih Sung (Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University) ;
  • Park, Kwang Suk (Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University)
  • 이원규 (서울대학교 대학원 협동과정 바이오엔지니어링 전공) ;
  • 이홍지 (서울대학교 대학원 협동과정 바이오엔지니어링 전공) ;
  • 윤희남 (서울대학교 대학원 협동과정 바이오엔지니어링 전공) ;
  • 정기성 (서울대학교 대학원 협동과정 바이오엔지니어링 전공) ;
  • 박광석 (서울대학교 대학원 협동과정 바이오엔지니어링 전공)
  • Received : 2014.05.27
  • Accepted : 2014.08.01
  • Published : 2014.08.30
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Abstract

Recent technological advances have increased interest in personal health monitoring. Electrocardiogram(ECG) monitoring is a basic healthcare activity and can provide decisive information regarding cardiovascular system status. In this study, we developed a capacitive ECG measurement system that can be included within a cloth mattress pad. The device permits ECG data to be obtained during sleep by using capacitive electrodes. However, it is difficult to detect R-wave peaks automatically because signals obtained from the system can include a high level of noise from various sources. Because R-peak detection is important in ECG applications, we developed an algorithm that can reduce noise and improve detection accuracy under noisy conditions. Algorithm reliability was evaluated by determining its sensitivity(Se), positive predictivity(+P), and error rate(Er) by using data from the MIT-BIH Polysomnographic Database and from our capacitive ECG system. The results showed that Se = 99.75%, +P = 99.77%, and Er = 0.47% for MIT-BIH Polysomnographic Database while Se = 96.47%, +P = 99.32%, and Er = 4.34% for our capacitive ECG system. Based on those results, we conclude that our R-peak detection method is capable of providing useful ECG information, even under noisy signal conditions.

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