CHAPTER IX. CONCLUSIONS AND FUTURE WORKS
9.2.4 Applying Improvements of ELM in Medical Diagnosis
The neural networks have applied successfully in data mining, biomedicine and biomedical applications due to their abilities to resolve problems that are very difficult to handle by classical methods. Traditional training algorithms based on gradient-descent have some problems such as over-fitting, learning rate, epochs , etc..
The ELM algorithm can overcome these problems but often requires a large number of hidden units. Therefore, the improvements of ELM in our research can be effective applications of SLFNs in data mining, biomedicine and biomedical applications.
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전기화학 바이오센서에서 발생되는 변환전류곡선의 선형 및 비선형 분석
HUYNH TRUNG HIEU
전남대학교대학원 컴퓨터정보통신공학과
(지도교수: 원용관)
(국문초록)
과학과 기술의 발달 덕택에, 광범위한 진단 시험을 정교한 실험실 장비들 없이도빠르고 간편하게 수행이 가능해졌으며, 이때 바이오 센서가 기술적으로 주요 역할을 한다. 바이오센서들은 복잡한 혼합물 및 분화합물의 분석 및 결정에 있어 화학 및 생화확 산업 뿐 아니라 의약 및 건강 산업 분야에서도 매우 유용하다. 일반적으로, 바이오센서들은 정확도, 비용, 가용성, 범위 및 간편성 등과 같은 기능적 특성과 디자인에 따라 분류 및 평가된다. 이러한 기준에 비춰, 가격과 가용성 측면에서 있어서, 과 같은 디자인 그리고 기능 특성에 기초를 두어 분류되고 평가될 수 있다. 이 기초에, 전기화학 바이오 센서는 정확성, 비용및 가용성 때문에 선호된다.
수 많은 노력을 들인 결과로, 혈중 포도당 측정을 위한 전기화학적 바이오센서는 현재 상업적으로 널리 활용되는 바이오센서가 되고 있다.
그들은 휴대형 기기와 함께 당뇨병 환자의 혈중 포도당 농도를 정상 수치에 가깝도록 유지하기 위하여 혈중 포도당 농도 수준을 감시에 적용되며, 이는 당뇨병으로 인한 합병증을 감소시킬 수 있다. 비록, 휴대형 기기가 혈중 포도당 농도를 감시하고 제어하기 위하여 편리하게 사용되고 있으나, 그들의 정확도는 요산 아스코르빈산 산, PO2, PCO2, pH, 헤마토크릿 등의 간섭에 의하여 크게 영향을 받으며, 그 중에서도 헤마토크릿이 휴대형 장치에 의한 측정에 가장 크게 영향을 미친다. 한편, 이러한 산화 가능 물질에 의한 간섭은 화학 방법으로 감소되는 수 있으나, 헤마토크릿의 간섭 영향을 줄이기 위한 소수의 현실적인 해결 방법만이 제안되었다. 그러나, 이러한 해결 방법은 바이오센서 제조를 위한 공정 절차를 복잡성과 가격을 상승시키며, 휴대형 기기로의 구현 또한 어렵다.
본 연구는 전기화학 바이오센서를 이용한 혈중 포도당 측정에 있어 휴대형 측정기의 정확도를 개선하기 위한 지능컴퓨팅 방법의 개발에 집중한다. 혈중 포도당 농도 측정에 있어서의 전기화학 바이오센서의 분석 원리는 생물학적 상호반응 과정에 근간을 두고 있는데, 혈중 포도당과 포도당 산화효소와의 상호작용과 전극에 의해 단순화된 효소 형태의 산화에 의하여 변환전류라 불리는 전기화학적 전류 신호가 발생된다. 이러한 변환전류는 시간에 따라 변화하며, 이는 변환전류 곡선(TCC)라
불리는 곡선으로 표현된다. 변환전류 곡선(TCC)는 어떤 방법으로든
휴대형 측정기를 이용한 포도당 농도의 결정에 사용되어 왔다. 그러나, 본
연구는 변환전류 곡선(TCC)의 변화 형태가 포도당에 대한 정보뿐만
아니라 간섭을 포함한 다양한 인자들도 포함한다는 믿음에서 출발하였다.
따라서, 변환전류 곡선(TCC)의 분석은 전기화학 바이오 센서를 사용한
측정의 성능을 개선하는데 결정적인 역할을 할 수 있다.
본 연구에서는, 변환전류 곡선을 분석하기 위하여 Support Vector Machines
(SVM) 과 신경회로망을 포함하는 선형 및 비선형 모델들이 연구된다.
그들은 헤마토크릿과 같은 결정적 요인들을 결정하고 전혈의 포도당 측정에 대한 정확도를 증가시키기 위한 적절한 방법을 제공할 수 있다. 이러한 모델들은 간단하고 복잡한 화학적 과정을 요구하지 않으며, 휴대형 기기의 가격을 낮춘다.
변환전류 곡선으로부터 헤마토크릿을 추정하기 위한 새로운 방법들이 개발되었다. 첫째가 변환전류 곡선으로부터 선택된 점들의 선형 조합에 의하여 헤마토크릿을 추정하는 선형 모델들이다. 둘째 방법으로는 극단학습기계(ELM: Extreme Learning Machine) 알고리즘에 의하여 학습되는 단일 은닉층 전방향 신경회로망(SLFN: Single Hidden Layer Feedforward
Neural network)과 작고 간결한 회로망을 얻기 위한 이의 개선 방법들을
사용하는 것이다. 입력 특징은 전류변환 곡선늬 선정점들이다. 전류 선정점을 헤마토크릿으로 사상하기 위하여 Support Vector Machines (SVM)이 사용되는 방법이 세번째이다. 본 연구의 제안 방법으로 측정된 결과는 휴대형 측정기기 상에서 헤마토크릿의 영향을 줄이거나 제거하기 위한 중요한 요소이다. 더불어, 진단의학 표지자를 값이 저렴한 휴대형 기기를 이용하여 신속하게 측정할 수 있음을 보여준다.
또한, 휴대형 측정기기를 이용하여 헤마토크릿의 영향을 줄임으로써 혈중 혈당 농도 측정의 정확도를 높이기 위한 방법을 연구하였다. 이를 위하여, 헤마토크릿 농도와 관련된 오류 분포 함수를 회귀분석을 통하여