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 language | English |
|---|---|
| Title of host publication | Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017 |
| Editors | Lei Wang, Minghui Dong, Yanfeng Lu, Haizhou Li |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 65-69 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538632758 |
| DOIs | |
| Publication status | Published - 2018 Apr 10 |
| Event | 5th International Conference on Orange Technologies, ICOT 2017 - Singapore, Singapore Duration: 2017 Dec 8 → 2017 Dec 10 |
Publication series
| Name | Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017 |
|---|---|
| Volume | 2018-January |
Other
| Other | 5th International Conference on Orange Technologies, ICOT 2017 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 17-12-08 → 17-12-10 |
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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
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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 proceeding › Conference contribution
TY - GEN
T1 - Exploring microscopic fluctuation of facial expression for mood disorder classification
AU - Su, Ming Hsiang
AU - Wu, Chung-Hsien
AU - Huang, Kun Yi
AU - Hong, Qian Bei
AU - Wang, Hsin Min
PY - 2018/4/10
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.
UR - http://www.scopus.com/inward/record.url?scp=85048561858&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048561858&partnerID=8YFLogxK
U2 - 10.1109/ICOT.2017.8336090
DO - 10.1109/ICOT.2017.8336090
M3 - Conference contribution
T3 - Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
SP - 65
EP - 69
BT - Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
A2 - Wang, Lei
A2 - Dong, Minghui
A2 - Lu, Yanfeng
A2 - Li, Haizhou
PB - Institute of Electrical and Electronics Engineers Inc.
ER -