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

研究成果: Conference contribution

1 引文 (Scopus)

摘要

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.

原文English
主出版物標題Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
編輯Lei Wang, Minghui Dong, Yanfeng Lu, Haizhou Li
發行者Institute of Electrical and Electronics Engineers Inc.
頁面65-69
頁數5
ISBN(電子)9781538632758
DOIs
出版狀態Published - 2018 四月 10
事件5th International Conference on Orange Technologies, ICOT 2017 - Singapore, Singapore
持續時間: 2017 十二月 82017 十二月 10

出版系列

名字Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
2018-January

Other

Other5th International Conference on Orange Technologies, ICOT 2017
國家Singapore
城市Singapore
期間17-12-0817-12-10

指紋

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)

引用此文

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. 於 L. Wang, M. Dong, Y. Lu, & H. Li (編輯), Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017 (頁 65-69). (Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017; 卷 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. 編輯 / Lei Wang ; Minghui Dong ; Yanfeng Lu ; Haizhou Li. Institute of Electrical and Electronics Engineers Inc., 2018. 頁 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.",
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Su, MH, Wu, CH, Huang, KY, Hong, QB & Wang, HM 2018, Exploring microscopic fluctuation of facial expression for mood disorder classification. 於 L Wang, M Dong, Y Lu & H Li (編輯), Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017. Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017, 卷 2018-January, Institute of Electrical and Electronics Engineers Inc., 頁 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. 編輯 / 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; 卷 2018-January).

研究成果: Conference contribution

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AU - Wang, Hsin Min

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Y1 - 2018/4/10

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

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ER -

Su MH, Wu CH, Huang KY, Hong QB, Wang HM. Exploring microscopic fluctuation of facial expression for mood disorder classification. 於 Wang L, Dong M, Lu Y, Li H, 編輯, 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