@inproceedings{84d87da49b2b44d198bf371611ef3d3f,
title = "Implementation of a deep learning model for emotion evaluation based on LSTM psychological and physiological data",
abstract = "Evaluating mental status is an important issue that diagnosing depression. Hamilton Depression Rating Scale (HAM-D) is a common method to diagnosis depression. Generally, psychiatrists collect Monthly Mood Chart (MMC) to infer mental status of patients during treatments. However, the processes waste a lot of time. Therefore, our target is to find a method that can evaluate mental status faster. We'd used the constructed platform[15] to collect physiological and psychological data. We'd collected 91 data including 42 remission data and 49 non-remission data. We'd used Electroencephalography(EEG) to train LSTM model, and then got 70% accuracy. This model can automatically infer mood status that helping psychiatrists evaluating. This system had coordinated with two hospitals to refer mood status in the future.",
keywords = "Big data, Cloud, Depression, EEG, HRV, Precision medicine",
author = "Ting, {Yen Wei} and Zhang, {Yun Jie} and Chung, {Kuo Hsuan} and Chang, {Yue Shan}",
note = "Funding Information: ACKNOWLEDGMENT This project thanks to the sponsorship of the Ministry of Science and Technology Project, the project number is 108-2221-E-305 -013 -MY3. Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 International Automatic Control Conference, CACS 2021 ; Conference date: 03-11-2021 Through 06-11-2021",
year = "2021",
doi = "10.1109/CACS52606.2021.9639060",
language = "English",
series = "2021 International Automatic Control Conference, CACS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 International Automatic Control Conference, CACS 2021",
address = "United States",
}