Abstract
Electroencephalography (EEG)-based depression detection has become a hot topic in the development of biomedical engineering. However, the complexity and nonstationarity of EEG signals are two biggest obstacles to this application. In addition, the generalization of detection algorithms may be degraded owing to the influences brought by individual differences. In view of the correlation between EEG signals and individual demographics, such as gender, age, etc., and influences of these demographic factors on the incidence of depression, it would be better to incorporate demographic factors during EEG modeling and depression detection. In this work, we constructed an one-dimensional Convolutional Neural Network (1-D CNN) to obtain more effective features of EEG signals, then integrated gender and age factors into the 1-D CNN via an attention mechanism, which could prompt our 1-D CNN to explore complex correlations between EEG signals and demographic factors, and generate more effective high-level representations ultimately for the detection of depression. Experimental results on 170 (81 depressed patients and 89 normal controls) subjects showed that the proposed method is superior to the unitary 1-D CNN without gender and age factors and two other ways of incorporating demographics. This work also indicates that organic mixture of EEG signals and demographic factors is promising for the detection of depression.Clinical relevance-This work indicates that organically mixture of EEG signals and demographic factors is promising for the detection of depression.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.