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학술대회 Data filtering for corrupted MIMIC III dataset with deep learning
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저자
진용식, 신우상, 권우경, 김규형, 윤종필
발행일
202010
출처
International Conference on Control, Automation and Systems (ICCAS) 2020, pp.947-949
DOI
https://dx.doi.org/10.23919/ICCAS50221.2020.9268224
협약과제
20ZD1100, 대경권 지역산업 기반 ICT 융합기술 고도화 지원사업, 문기영
초록
In this paper, we propose a corrupted data filtering method for MIMIC III dataset based on the convolutional autoencoder. The convolutional autoencoder is employed to restore the corrupted data, and using the restoration error, the degree of data contamination is judged. Based on this function, a corrupted data filtering algorithm is constructed, and arterial blood pressure (ABP) and photoplethysmogram (PPG) signals are filtered. The experimental results show the effectiveness of the proposed method.
키워드
Convolutional autocoder, Corrupted data filtering, Deep learning, Medical IT, MIMIC III database
KSP 제안 키워드
Convolutional auto-encoder, Filtering algorithms, Filtering method, arterial blood pressure(ABP), data contamination, data filtering, deep learning(DL)