상세정보
학술대회
Data filtering for corrupted MIMIC III dataset with deep learning
- 발행일
- 202010
- 출처
- International Conference on Control, Automation and Systems (ICCAS) 2020, pp.947-949
- 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.
- KSP 제안 키워드
- Convolutional auto-encoder, Filtering algorithms, Filtering method, arterial blood pressure(ABP), data contamination, data filtering, deep learning(DL)