TY - GEN
T1 - Teaching Machines to Know Your Depressive State
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
AU - Qian, Kun
AU - Kuromiya, Hiroyuki
AU - Zhang, Zixing
AU - Kim, Jinhyuk
AU - Nakamura, Toru
AU - Yoshiuchi, Kazuhiro
AU - Schuller, Bjorn W.
AU - Yamamoto, Yoshiharu
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - A less-invasive method for the diagnosis of the major depressive disorder can be useful for both the psychiatrists and the patients. We propose a machine learning framework for automatically discriminating patients suffering from the major depressive disorder (n = 14) and healthy subjects (n = 17). To this end, spontaneous physical activity data were recorded via a watch-type computer device equipped by the participants in their daily lives. Two machine learning models are investigated and compared, i. e., support vector machines, and deep recurrent neural networks. Experimental results show that, both of the two methods, i. e., the static model fed with human hand-crafted features, and the sequential model fed with raw data can reach a promising performance with an unweighted average recall at 76.0 % and 56.3 %, respectively.
AB - A less-invasive method for the diagnosis of the major depressive disorder can be useful for both the psychiatrists and the patients. We propose a machine learning framework for automatically discriminating patients suffering from the major depressive disorder (n = 14) and healthy subjects (n = 17). To this end, spontaneous physical activity data were recorded via a watch-type computer device equipped by the participants in their daily lives. Two machine learning models are investigated and compared, i. e., support vector machines, and deep recurrent neural networks. Experimental results show that, both of the two methods, i. e., the static model fed with human hand-crafted features, and the sequential model fed with raw data can reach a promising performance with an unweighted average recall at 76.0 % and 56.3 %, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85077849977&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2019.8857838
DO - 10.1109/EMBC.2019.8857838
M3 - Conference contribution
C2 - 31946654
AN - SCOPUS:85077849977
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3592
EP - 3595
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 July 2019 through 27 July 2019
ER -
