대한의용생체공학회:의공학회지 (Journal of Biomedical Engineering Research)

  • 제17권3호
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  • Pages.387-394
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  • 1996
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  • 1229-0807(pISSN)
  • /
  • 2288-9396(eISSN)
대한의용생체공학회 (The Korean Society of Medical and Biological Engineering)
권호 표지

적응 학습방식의 신경망을 이용한 좌심실보조장치의 모델링

Adaptively Trained Artificial Neural Network Identification of Left Ventricular Assist Device

  • 김상현 (연세대학교 의과대학 심혈관연구소) ;
  • 김훈모 (성균관대학교 공과대학 기계설계학과) ;
  • 류정우 (성균관대학교 공과대학 기계설계학과 대학원)
  • Kim, Sang-Hyun (Cardiovascular Research Institute, Yonsei University College of Medicine) ;
  • Kim, Hun-Mo (Department of Mechnical Design, College of Engineering, Sungkyunkwan University) ;
  • Ryu, Jung-Woo
  • 발행 : 1996.09.01
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초록

This paper presents a Neural Network Identification(NNI) method for modeling of highly complicated nonlinear and time varing human system with a pneumatically driven mock circulatory system of Left Ventricular Assist Device(LVAD). This system consists of electronic circuits and pneumatic driving circuits. The initiation of systole and the pumping duration can be determined by the computer program. The line pressure from a pressure transducer inserted in the pneumatic line was recorded System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, heart rate(HR), systole-diastole rate(SDR), which can vary state of system. Output parameters are preload, afterload which indicate the systemic dynamic characteristics. Consequently, the neural network shows good approximation of nonlinearity, and characteristics of left Ventricular Assist Device. Our results show that the neural network leads to a significant improvement in the modeling of highly nonlinear Left Ventricular Assist Device.

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참고문헌

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