전자공학회논문지CI (Journal of the Institute of Electronics Engineers of Korea CI)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

심전도기반 u-Healthcare 시스템을 위한 파형추출 방법

Development of Signal Detection Methods for ECG (Electrocardiogram) based u-Healthcare Systems

  • 민철홍 (가톨릭대학교 컴퓨터공학과) ;
  • 김태선 (가톨릭대학교 정보통신전자공학부)
  • Min, Chul-Hong (Dept. of Computer Science and Eng., The Catholic University of Korea) ;
  • Kim, Tae-Seon (School of Information, Communications and Electronics Eng., The Catholic University of Korea)
  • 발행 : 2009.11.25
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초록

본 논문에서는 심전도 기반의 u헬스케어시스템을 위한 다용도 신호추출 방법을 제안한다. 심전도 기반의 u헬스케어시스템 구현을 위해서는 심장질환 진단을 위한 QRS파형의 추출기술이 필수적이다. 또한, 보안성 및 편의성을 위하여 u헬스케어시스템에서 ECG신호와 같은 생체신호에서 직접 사용자의 신원을 확인할 수 있는 생체인식기능을 보유하고 있다면 매우 유용하다. 이를 위해서 본 논문에서는, 리드II 파형으로부터 QRS파형을 추출하고, 또한 상대적으로 노화 및 질환에 따른 변동에 강건한 리드III 파형으로부터 생체인식을 위한 신호추출법을 제안한다. 리드II 파형으로부터 QRS신호추출성능을 검증하기 위해 MIT-BIH 데이터베이스의 심전도신호가 사용되었고 99.36%의 정확도 및 99.68%의 민감도성능을 보였다. 또한 생체인식용 신호추출성능평가를 위해서는 다양한 측정환경을 고려하기 위해 음주, 흡연 및 운동 직후 리드III파형이 측정되었고 99.92%의 정확도 및 99.97%의 민감도 성능을 보였다.

In this paper, we proposed multipurpose signal detection methods for ECG (electrocardiogram) based u-healthcare systems. For ECG based u-healthcare system, QRS signal extraction for cardiovascular disease diagnosis is essential. Also, for security and convenience reasons, it is desirable if u-healthcare system support biometric identification directly from user's bio-signal such as ECG for this case. For this, from Lead II signal, we developed QRS signal detection method and also, we developed signal extraction method for biometric identification using Lead II signal which is relatively robust from signal alteration by aging and diseases. For QRS signal detection capability from Lead II signal, ECG signals from MIT-BIH database are used and it showed 99.36% of accuracy and 99.68% of sensitivity. Also, to show the performance of signal extraction capability for biometric diagnosis purpose, Lead III signals are measured after drinking, smoking, or exercise to consider various monitoring conditions and it showed 99.92% of accuracy and 99.97% of sensitivity.

키워드

참고문헌

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