• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.6
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• pp.43-50
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• 2011

Currently, conventional security methods including IC card or password type method are quickly switched into biometric security systems in various applications and the electrocardiogram (ECG) has been considered as one of novel biometrics way. However, conventional ECG based biometrics used lead II signal which conventionally used for formulaic signal to heart disease diagnosis and it is not suitable for biometrics since it is rather difficult to find consistent features for heart disease patents. To overcome this problem, we developed new biometrics system using ECG lead III signals. For wave extraction, signal peak points are extracted through AAV algorithm. For feature selection, extracted waves are categorized into one of four wave types and total twenty two features including number of vertices, wave shapes, amplitude information and interval information are extracted based on their wave types. Experimental results for thirty-six people showed 100% specificity, 95.59% sensitivity and 99.17% of overall identification accuracy.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.18-26
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• 2009

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.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.791-792
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• 2006

본 논문은 심전도의 리드III 파형을 이용하여 신원확인이 가능한 생체인식 기술을 제안한다. 인식을 위한 심전도의 리드III파형을 특징추출하기 위해 $4{\sim}30Hz$의 대역통과 필터를 사용하여 피크(peak)점만 남겨놓고 모든 잡음을 제거한 후, AAV(absolute amplitude value)를 이용하여 피크점의 값을 추출한다. 추출된 피크 점은 원신호의 피크점과 같으므로 이를 기준으로 전체파형을 특징추출을 위한 단위 파형으로 분리한다. 분리된 신호는 정의된 4가지 형태(type)의 파형 중 가장 유사한 파형타입으로 분류되며, 분류된 형태를 기준으로 꼭지점, 최대 피크점, 최소 피크점, 최대.최소 피크점 비, 파형 간격(interval) 및 파형의 세부 모양 등 총22가지의 특징들을 추출한다. 추출된 특징들은 오류역전파 신경회로망(back-propagation neural network)의 입력으로 사용되었으며, 성인남녀 31명을 대상으로 제한된 파형 내에서 실험한 결과 100%의 인식률을 보였다.