中图分类号： TP18
文献标识码： A
DOI： 10.19358/j.issn.2096-5133.2022.05.011
引用格式： 穆家宝. 结合注意力机制的CNN-LSTM的视频中双相抑郁症检测方法[J].信息技术与网络安全，2022，41(5)：72-76.

Bipolar disorder detection in videos by integrating attention mechanism based on CNN-LSTM

Abstract： Bipolar disorder can prevent people from participating in normal social life because of severe emotional problems, and even lead to self-harm and suicidal behavior. In order to accurately detect the patient′s current psychological state to assist doctors in more accurate treatment，in this paper, a hybrid model of CNN-LSTM network combined with attention mechanism is proposed for bipolar depression detection. The method firstly uses the Resnet50 model fine-tuned on the facial expression dataset to extract the spatial features of video frames, and secondly uses the LSTM network combined with the attention mechanism to extract the temporal information between frames to detect bipolar depression. The effectiveness of this method was verified on the development set of the AVEC2018 bipolar depression database.

Key words : bipolar disorder detection；convolutional neural network；long short-term memory