Optimization of 1D CNN Model Factors for ECG Signal Classification (심전도 신호 분류를 위한 1D CNN 모델 구성 요소의 최적화)

Hyun-Ji Lee (이현지); Hyeon-Ah Kang (강현아); Seung-Hyun Lee (이승현); Chang-Hyun Lee (이창현); Seung Bo Park (박승보)

doi:10.9708/jksci.2021.26.07.029

Optimization of 1D CNN Model Factors for ECG Signal Classification

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2021, 26(7), pp.29-36
DOI : 10.9708/jksci.2021.26.07.029
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : July 9, 2021
Accepted : July 26, 2021
Published : July 30, 2021

Hyun-Ji Lee ¹, Hyeon-Ah Kang ¹, Seung-Hyun Lee ¹, Chang-Hyun Lee ¹, Seung Bo Park ¹

¹인하대학교

Accredited

ABSTRACT

In this paper, we classify ECG signal data for mobile devices using deep learning models. To classify abnormal heartbeats with high accuracy, three factors of the deep learning model are selected, and the classification accuracy is compared according to the changes in the conditions of the factors. We apply a CNN model that can self-extract features of ECG data and compare the performance of a total of 48 combinations by combining conditions of the depth of model, optimization method, and activation functions that compose the model. Deriving the combination of conditions with the highest accuracy, we obtained the highest classification accuracy of 97.88% when we applied 19 convolutional layers, an optimization method SGD, and an activation function Mish. In this experiment, we confirmed the suitability of feature extraction and abnormal beat detection of 1-channel ECG signals using CNN.

KEYWORDS

Deep learning, Electrocardiogram, CNN, ResNet, Arrhythmia Detection

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* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Optimization of 1D CNN Model Factors for ECG Signal Classification

ABSTRACT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Optimization of 1D CNN Model Factors for ECG Signal Classification

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.