Data filtering for corrupted MIMIC III dataset with deep learning
| Autor: | Kyuhyung Kim, Yongsik Jin, Jong Pil Yun, Wookyong Kwon, Crino Shin |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science business.industry Deep learning 020208 electrical & electronic engineering Pattern recognition 02 engineering and technology Autoencoder Convolution Data modeling Data filtering 020901 industrial engineering & automation Photoplethysmogram 0202 electrical engineering electronic engineering information engineering Artificial intelligence business |
| Zdroj: | 2020 20th International Conference on Control, Automation and Systems (ICCAS). |
| DOI: | 10.23919/iccas50221.2020.9268224 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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