한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제26권1호
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  • Pages.30-40
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  • 2022
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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비정상심박 검출을 위해 영상화된 심전도 신호를 이용한 비교학습 기반 딥러닝 알고리즘

Comparative Learning based Deep Learning Algorithm for Abnormal Beat Detection using Imaged Electrocardiogram Signal

  • Bae, Jinkyung (Daegu Science High School) ;
  • Kwak, Minsoo (Daegu Science High School) ;
  • Noh, Kyeungkap (Daegu Science High School) ;
  • Lee, Dongkyu (School of Electronic and Electrical Engineering, Kyungpook National University) ;
  • Park, Daejin (School of Electronic and Electrical Engineering, Kyungpook National University) ;
  • Lee, Seungmin (School of Electronic and Electrical Engineering, Kyungpook National University)
  • 투고 : 2021.10.13
  • 심사 : 2021.12.08
  • 발행 : 2022.01.31
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초록

심전도 신호는 개인에 따라 형태와 특징이 다양하므로, 하나의 신경망으로는 분류하기가 어렵다. 주어진 데이터를 직접적으로 분류하는 것은 어려우나, 대응되는 정상 데이터가 있을 경우, 이를 비교하여 정상 및 비정상을 분류하는 것은 상대적으로 쉽고 정확하다. 본 논문에서는 템플릿 군을 이용하여 대표정상심박 정보를 획득하고, 이를 입력 심박에 결합함으로써 심박을 분류한다. 결합된 심박을 영상화한 후, 학습 및 분류를 진행하여, 하나의 신경망으로도 다양한 레코드의 비정상심박을 검출이 가능하였다. 특히, GoogLeNet, ResNet, DarkNet 등 다양한 신경망에 대해서도 비교학습 기법을 적용한 결과, 모두 우수한 검출성능을 가졌으며, GoogLeNet의 경우 99.72%의 민감도로, 실험에 사용된 신경망 중 가장 우수한 성능을 가졌음을 확인하였다.

Electrocardiogram (ECG) signal's shape and characteristic varies through each individual, so it is difficult to classify with one neural network. It is difficult to classify the given data directly, but if corresponding normal beat is given, it is relatively easy and accurate to classify the beat by comparing two beats. In this study, we classify the ECG signal by generating the reference normal beat through the template cluster, and combining with the input ECG signal. It is possible to detect abnormal beats of various individual's records with one neural network by learning and classifying with the imaged ECG beats which are combined with corresponding reference normal beat. Especially, various neural networks, such as GoogLeNet, ResNet, and DarkNet, showed excellent performance when using the comparative learning. Also, we can confirmed that GoogLeNet has 99.72% sensitivity, which is the highest performance of the three neural networks.

키워드

과제정보

This study was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1A6A1A03 025109, 15%, NRF-2020R1I1A1A01072343, 15%), and Daegu Science High School 2nd year self-research program (50%). This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-00944, Metamorphic approach of unstructured validation/verification for analyzing binary code, 20%). These authors J. Bae, M. Kwak, and K. Noh are equally contributed.

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