Exploring microscopic fluctuation of facial expression for mood disorder classification

Ming Hsiang Su, Chung-Hsien Wu, Kun Yi Huang, Qian Bei Hong, Hsin Min Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In clinical diagnosis of mood disorder, depression is one of the most common psychiatric disorders. There are two major types of mood disorders: major depressive disorder (MDD) and bipolar disorder (BPD). A large portion of BPD are misdiagnosed as MDD in the diagnostic of mood disorders. Short-term detection which could be used in early detection and intervention is thus desirable. This study investigates microscopic facial expression changes for the subjects with MDD, BPD and control group (CG), when elicited by emotional video clips. This study uses eight basic orientations of motion vector (MV) to characterize the subtle changes in microscopic facial expression. Then, wavelet decomposition is applied to extract entropy and energy of different frequency bands. Next, an autoencoder neural network is adopted to extract the bottleneck features for dimensionality reduction. Finally, the long short term memory (LSTM) is employed for modeling the long-term variation among different mood disorders types. For evaluation of the proposed method, the elicited data from 36 subjects (12 for each of MDD, BPD and CG) were considered in the K-fold (K=12) cross validation experiments, and the performance for distinguishing among MDD, BPD and CG achieved 67.7% accuracy.

Original languageEnglish
Title of host publicationProceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
EditorsLei Wang, Minghui Dong, Yanfeng Lu, Haizhou Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages65-69
Number of pages5
ISBN (Electronic)9781538632758
DOIs
Publication statusPublished - 2018 Apr 10
Event5th International Conference on Orange Technologies, ICOT 2017 - Singapore, Singapore
Duration: 2017 Dec 82017 Dec 10

Publication series

NameProceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
Volume2018-January

Other

Other5th International Conference on Orange Technologies, ICOT 2017
CountrySingapore
CitySingapore
Period17-12-0817-12-10

Fingerprint

moods
Facial Expression
facial expression
Major Depressive Disorder
Mood Disorders
Bipolar Disorder
mood
fluctuation
disorders
Wavelet decomposition
Control Groups
video clip
Frequency bands
Group
Entropy
entropy
neural network
Long-Term Memory
Neural networks
diagnostic

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Instrumentation
  • Computer Networks and Communications
  • Computer Science Applications
  • Human-Computer Interaction
  • Information Systems
  • Health(social science)

Cite this

Su, M. H., Wu, C-H., Huang, K. Y., Hong, Q. B., & Wang, H. M. (2018). Exploring microscopic fluctuation of facial expression for mood disorder classification. In L. Wang, M. Dong, Y. Lu, & H. Li (Eds.), Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017 (pp. 65-69). (Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICOT.2017.8336090
Su, Ming Hsiang ; Wu, Chung-Hsien ; Huang, Kun Yi ; Hong, Qian Bei ; Wang, Hsin Min. / Exploring microscopic fluctuation of facial expression for mood disorder classification. Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. editor / Lei Wang ; Minghui Dong ; Yanfeng Lu ; Haizhou Li. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 65-69 (Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017).
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title = "Exploring microscopic fluctuation of facial expression for mood disorder classification",
abstract = "In clinical diagnosis of mood disorder, depression is one of the most common psychiatric disorders. There are two major types of mood disorders: major depressive disorder (MDD) and bipolar disorder (BPD). A large portion of BPD are misdiagnosed as MDD in the diagnostic of mood disorders. Short-term detection which could be used in early detection and intervention is thus desirable. This study investigates microscopic facial expression changes for the subjects with MDD, BPD and control group (CG), when elicited by emotional video clips. This study uses eight basic orientations of motion vector (MV) to characterize the subtle changes in microscopic facial expression. Then, wavelet decomposition is applied to extract entropy and energy of different frequency bands. Next, an autoencoder neural network is adopted to extract the bottleneck features for dimensionality reduction. Finally, the long short term memory (LSTM) is employed for modeling the long-term variation among different mood disorders types. For evaluation of the proposed method, the elicited data from 36 subjects (12 for each of MDD, BPD and CG) were considered in the K-fold (K=12) cross validation experiments, and the performance for distinguishing among MDD, BPD and CG achieved 67.7{\%} accuracy.",
author = "Su, {Ming Hsiang} and Chung-Hsien Wu and Huang, {Kun Yi} and Hong, {Qian Bei} and Wang, {Hsin Min}",
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Su, MH, Wu, C-H, Huang, KY, Hong, QB & Wang, HM 2018, Exploring microscopic fluctuation of facial expression for mood disorder classification. in L Wang, M Dong, Y Lu & H Li (eds), Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 65-69, 5th International Conference on Orange Technologies, ICOT 2017, Singapore, Singapore, 17-12-08. https://doi.org/10.1109/ICOT.2017.8336090

Exploring microscopic fluctuation of facial expression for mood disorder classification. / Su, Ming Hsiang; Wu, Chung-Hsien; Huang, Kun Yi; Hong, Qian Bei; Wang, Hsin Min.

Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. ed. / Lei Wang; Minghui Dong; Yanfeng Lu; Haizhou Li. Institute of Electrical and Electronics Engineers Inc., 2018. p. 65-69 (Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - In clinical diagnosis of mood disorder, depression is one of the most common psychiatric disorders. There are two major types of mood disorders: major depressive disorder (MDD) and bipolar disorder (BPD). A large portion of BPD are misdiagnosed as MDD in the diagnostic of mood disorders. Short-term detection which could be used in early detection and intervention is thus desirable. This study investigates microscopic facial expression changes for the subjects with MDD, BPD and control group (CG), when elicited by emotional video clips. This study uses eight basic orientations of motion vector (MV) to characterize the subtle changes in microscopic facial expression. Then, wavelet decomposition is applied to extract entropy and energy of different frequency bands. Next, an autoencoder neural network is adopted to extract the bottleneck features for dimensionality reduction. Finally, the long short term memory (LSTM) is employed for modeling the long-term variation among different mood disorders types. For evaluation of the proposed method, the elicited data from 36 subjects (12 for each of MDD, BPD and CG) were considered in the K-fold (K=12) cross validation experiments, and the performance for distinguishing among MDD, BPD and CG achieved 67.7% accuracy.

AB - In clinical diagnosis of mood disorder, depression is one of the most common psychiatric disorders. There are two major types of mood disorders: major depressive disorder (MDD) and bipolar disorder (BPD). A large portion of BPD are misdiagnosed as MDD in the diagnostic of mood disorders. Short-term detection which could be used in early detection and intervention is thus desirable. This study investigates microscopic facial expression changes for the subjects with MDD, BPD and control group (CG), when elicited by emotional video clips. This study uses eight basic orientations of motion vector (MV) to characterize the subtle changes in microscopic facial expression. Then, wavelet decomposition is applied to extract entropy and energy of different frequency bands. Next, an autoencoder neural network is adopted to extract the bottleneck features for dimensionality reduction. Finally, the long short term memory (LSTM) is employed for modeling the long-term variation among different mood disorders types. For evaluation of the proposed method, the elicited data from 36 subjects (12 for each of MDD, BPD and CG) were considered in the K-fold (K=12) cross validation experiments, and the performance for distinguishing among MDD, BPD and CG achieved 67.7% accuracy.

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BT - Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017

A2 - Wang, Lei

A2 - Dong, Minghui

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PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Su MH, Wu C-H, Huang KY, Hong QB, Wang HM. Exploring microscopic fluctuation of facial expression for mood disorder classification. In Wang L, Dong M, Lu Y, Li H, editors, Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 65-69. (Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017). https://doi.org/10.1109/ICOT.2017.8336090