Depression Scale Prediction with Cross-Sample Entropy and Deep Learning

Guan Yen Chen, Chih Mao Huang, Ho Ling Liu, Shwu Hua Lee, Tatia Mei Chun Lee, Chemin Lin, Shun Chi Wu

研究成果: Conference contribution同行評審

摘要

A two-stage deep learning-based scheme is presented to predict the Hamilton Depression Scale (HAM-D) in this study. First, the cross-sample entropy (CSE) that allows assessing the degree of similarity of two data series are evaluated for the 90 brain regions of interest partitioned according to Automated Anatomical Labeling. The obtained CSE maps are then converted to 3D CSE volumes to serve as the inputs to the deep learning network models for the HAM-D scale level classification and prediction. The efficacy of the proposed scheme was illustrated by the resting-state functional magnetic resonance imaging data from 38 patients. From the results, the root mean square errors for the HAM-D scale prediction obtained during training, validation, and testing were 2.73, 2.66, and 2.18, which were less than those of a scheme having only a regression stage.

原文English
主出版物標題42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
主出版物子標題Enabling Innovative Technologies for Global Healthcare, EMBC 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面120-123
頁數4
ISBN(電子)9781728119908
DOIs
出版狀態Published - 20 七月 2020
事件42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada
持續時間: 20 七月 202024 七月 2020

出版系列

名字Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
2020-July
ISSN(列印)1557-170X

Conference

Conference42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
國家Canada
城市Montreal
期間20/07/2024/07/20

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