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

doi:10.1109/EMBC44109.2020.9175816

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

National Tsing Hua University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

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.

Original language	English
Title of host publication	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publication	Enabling Innovative Technologies for Global Healthcare, EMBC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	120-123
Number of pages	4
ISBN (Electronic)	9781728119908
DOIs	https://doi.org/10.1109/EMBC44109.2020.9175816
State	Published - 07 2020
Externally published	Yes
Event	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada Duration: 20 07 2020 → 24 07 2020

Publication series

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

Conference

Conference	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Country/Territory	Canada
City	Montreal
Period	20/07/20 → 24/07/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Access to Document

10.1109/EMBC44109.2020.9175816

Cite this

Chen, G. Y., Huang, C. M., Liu, H. L., Lee, S. H., Lee, T. M. C., Lin, C., & Wu, S. C. (2020). Depression Scale Prediction with Cross-Sample Entropy and Deep Learning. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020 (pp. 120-123). Article 9175816 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2020-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC44109.2020.9175816

Chen, Guan Yen ; Huang, Chih Mao ; Liu, Ho Ling et al. / Depression Scale Prediction with Cross-Sample Entropy and Deep Learning. 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 120-123 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Depression Scale Prediction with Cross-Sample Entropy and Deep Learning",

abstract = "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.",

author = "Chen, \{Guan Yen\} and Huang, \{Chih Mao\} and Liu, \{Ho Ling\} and Lee, \{Shwu Hua\} and Lee, \{Tatia Mei Chun\} and Chemin Lin and Wu, \{Shun Chi\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 ; Conference date: 20-07-2020 Through 24-07-2020",

year = "2020",

month = jul,

doi = "10.1109/EMBC44109.2020.9175816",

language = "英语",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

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Chen, GY, Huang, CM, Liu, HL, Lee, SH, Lee, TMC, Lin, C & Wu, SC 2020, Depression Scale Prediction with Cross-Sample Entropy and Deep Learning. in 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020., 9175816, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2020-July, Institute of Electrical and Electronics Engineers Inc., pp. 120-123, 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canada, 20/07/20. https://doi.org/10.1109/EMBC44109.2020.9175816

Depression Scale Prediction with Cross-Sample Entropy and Deep Learning. / Chen, Guan Yen; Huang, Chih Mao; Liu, Ho Ling et al.
42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 120-123 9175816 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2020-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Depression Scale Prediction with Cross-Sample Entropy and Deep Learning

AU - Chen, Guan Yen

AU - Huang, Chih Mao

AU - Liu, Ho Ling

AU - Lee, Shwu Hua

AU - Lee, Tatia Mei Chun

AU - Lin, Chemin

AU - Wu, Shun Chi

PY - 2020/7

Y1 - 2020/7

N2 - 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.

AB - 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.

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Chen GY, Huang CM, Liu HL, Lee SH, Lee TMC, Lin C et al. Depression Scale Prediction with Cross-Sample Entropy and Deep Learning. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 120-123. 9175816. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC44109.2020.9175816

Depression Scale Prediction with Cross-Sample Entropy and Deep Learning

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