Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model

윤덕용; 임홍석; 정경원; 김태영; 이석훈

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자료유형: 학술저널

저자정보: 윤덕용 임홍석 정경원 김태영 이석훈

저널정보: 대한의료정보학회 Healthcare Informatics Research Healthcare Informatics Research 제25권 제3호

발행연도: 2019.1

수록면: 201 - 211 (11page)

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Objectives: Biosignal data captured by patient monitoring systems could provide key evidence for detecting or predicting critical clinical events; however, noise in these data hinders their use. Because deep learning algorithms can extract features without human annotation, this study hypothesized that they could be used to screen unacceptable electrocardiograms (ECGs) that include noise. To test that, a deep learning-based model for unacceptable ECG screening was developed, and its screening results were compared with the interpretations of a medical expert. Methods: To develop and apply the screening model, we used a biosignal database comprising 165,142,920 ECG II (10-second lead II electrocardiogram) data gathered between August 31, 2016 and September 30, 2018 from a trauma intensive-care unit. Then, 2,700 and 300 ECGs (ratio of 9:1) were reviewed by a medical expert and used for 9-fold cross-validation (training and validation) and test datasets. A convolutional neural network-based model for unacceptable ECG screening was developed based on the training and validation datasets. The model exhibiting the lowest cross-validation loss was subsequently selected as the final model. Its performance was evaluated through comparison with a test dataset. Results: When the screening results of the proposed model were compared to the test dataset, the area under the receiver operating characteristic curve and the F1-score of the model were 0.93 and 0.80 (sensitivity = 0.88, specificity = 0.89, positive predictive value = 0.74, and negative predictive value = 0.96). Conclusions: The deep learning-based model developed in this study is capable of detecting and screening unacceptable ECGs efficiently.

참고문헌 (28)

참고문헌 신청

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