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Conference Paper Data filtering for corrupted MIMIC III dataset with deep learning
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Authors
Yongsik Jin, Crino Shin, Wookyong Kwon, Kyuhyung Kim, Jong Pil Yun
Issue Date
2020-10
Citation
International Conference on Control, Automation and Systems (ICCAS) 2020, pp.947-949
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.23919/ICCAS50221.2020.9268224
Abstract
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 Keywords
Convolutional auto-encoder, Filtering algorithms, Filtering method, arterial blood pressure(ABP), data contamination, data filtering, deep learning(DL)