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
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
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
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U2 - 10.1109/ICOT.2017.8336090
DO - 10.1109/ICOT.2017.8336090
M3 - Conference contribution
AN - SCOPUS:85048561858
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 - Dong, Minghui
A2 - Wang, Lei
A2 - Lu, Yanfeng
A2 - Li, Haizhou
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Orange Technologies, ICOT 2017
Y2 - 8 December 2017 through 10 December 2017
ER -