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IJMLC 2021 Vol.11(1): 55-60 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.1.1014

Bipolar Disorder Classification Based on Multimodal Recordings

Bo Sun, Siming Cao, Penghao Rao, Jun He, Lejun Yu, and Yongkang Xiao

Abstract—Automatic bipolar disorder classification is a challenging task. In this paper, we mainly focus on BD classification from acoustic, visual, and textual modalities. We highlight three aspects of our methods: 1) besides the baseline features, we explore and fuse some hand-crafted and deep learned features from all available modalities including acoustic, visual, and textual modalities. It should be noted that we extracted the textual modality by using the voice translation tool according to the acoustic modality; 2) Considering the fact that each video is given only one video-level label, while each frame of the video is unlabeled, we use the unsupervised Convolutional Auto-Encoder (CAE) and used it for feature extraction. 3) Due to the dataset is too small to train Convolutional Neural Network (CNN), so we decide to pre-train the CNN on other emotion datasets. The experimental results show that our model outperforms the baseline system. The final unweighted average recall (UAR) we gained is 93.12%.

Index Terms—Bipolar disorder classification, CNN, CAE, multimodal features.

The authors are with the Computer Science Department, Beijing Normal University, Beijing, China (Corresponding author: Yongkang Xiao; e-mail: tosunbo@mail.bnu.edu.cn, caosiming@mail.bnu.edu.cn, 201721210016@mail.bnu.edu.cn, hejun@bnu.edu.cn, yulejun@bnu.edu.cn, xiaoyk@bnu.edu.cn).


Cite: Bo Sun, Siming Cao, Penghao Rao, Jun He, Lejun Yu, and Yongkang Xiao, "Bipolar Disorder Classification Based on Multimodal Recordings," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 55-60, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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