EEG-based mild depression recognition using convolutional neural network
Electroencephalography (EEG)–based studies focus on depression recognition using data mining methods, while those on mild depression are yet in infancy, especially in effective monitoring and quantitative measure aspects. Aiming at mild depression recognition, this study proposed a computer-aided detection (CAD) system using convolutional neural network (ConvNet). However, the architecture of ConvNet derived by trial and error and the CAD system used in clinical practice should be built on the basis of the local database; we therefore applied transfer learning when constructing ConvNet architecture. We also focused on the role of different aspects of EEG, i.e., spectral, spatial, and temporal information, in the recognition of mild depression and found that the spectral information of EEG played a major role and the temporal information of EEG provided a statistically significant improvement to accuracy. The proposed system provided the accuracy of 85.62% for recognition of mild depression and normal controls with 24-fold cross-validation (the training and test sets are divided based on the subjects). Thus, the system can be clinically used for the objective, accurate, and rapid diagnosis of mild depression.
KeywordsEEG Mild depression Convolutional neural network Transfer learning Classification
This work was supported by the National Basic Research Program of China (973 Program) [No. 2014CB744600], the National Natural Science Foundation of China [Grant No. 61632014, No. 61210010, and No. 61402211], the Fundamental Research Funds for the Central Universities [No. lzujbky-2017-it74 and No. lzujbky-2017-it75], the International Cooperation Project of Ministry of Science and Technology [No. 2013DFA11140], and the Program of Beijing Municipal Science & Technology Commission [No. Z171100000117005].
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- 8.Hu B, Rao J, Li X, Cao T, Li J, Majoe D, Gutknecht J (2017) Emotion regulating attentional control abnormalities in major depressive disorder: an event-related potential study. Sci Rep 7(13530)Google Scholar
- 10.Li X, Cao T, Sun S, Hu B, Ratcliffe M (2016) Classification study on eye movement data: towards a new approach in depression detection. In: Evolutionary Computation (CEC), IEEE Congress on. IEEE, pp 1227–1232. https://doi.org/10.1109/CEC.2016.7743927
- 18.Cai H, Han J, Chen Y, Sha X, Wang Z, Hu B, Yang J, Feng L, Ding Z, Chen Y (2018) A pervasive approach to EEG-based depression detection. Complexity 2018:1–13Google Scholar
- 19.Zhang X, Hu B, Zhou L, Moore P, Chen J2012 An EEG based pervasive depression detection for females. In: Joint International Conference on Pervasive Computing and the Networked World, Springer, pp 848–861Google Scholar
- 21.Hosseinifard B, Moradi MH, Rostami R (2011) Classifying depression patients and normal subjects using machine learning techniques. In: Electrical Engineering (ICEE),19th Iranian Conference on, 2011. IEEE, pp 1–4Google Scholar
- 23.Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:12070580.Google Scholar
- 24.Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L (2014) Large-scale video classification with convolutional neural networks. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 1725–1732Google Scholar
- 25.Zhang X, LeCun Y (2015) Text understanding from scratch. arXiv preprint arXiv:150201710Google Scholar
- 26.Schirrmeister RT, Springenberg JT, Fiederer LDJ, Glasstetter M, Eggensperger K, Tangermann M, Hutter F, Burgard W, Ball T (2017) Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG. arXiv preprint arXiv:170305051.Google Scholar
- 28.Tripathi S, Acharya S, Sharma RD, Mittal S, Bhattacharya S (2017) Using deep and convolutional neural networks for accurate emotion classification on DEAP dataset. In: AAAI, . pp 4746–4752Google Scholar
- 32.Zhang W, Li R, Zeng T, Sun Q, Kumar S, Ye J, Ji S (2016) Deep model based transfer and multi-task learning for biological image analysis. IEEE Trans Big DataGoogle Scholar
- 33.Bashivan P, Rish I, Yeasin M, Codella N (2015) Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv preprint arXiv:151106448Google Scholar
- 39.Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp 315–323Google Scholar
- 40.Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256Google Scholar
- 41.Kingma DP, Ba J Adam: (2014) A method for stochastic optimization. arXiv preprint arXiv:14126980.Google Scholar
- 42.Krizhevsky A, Sutskever I, Hinton GE (2012). Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105Google Scholar
- 43.Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, Johnson HJ, Paulsen JS, Turner JA, Calhoun VD (2014) Deep learning for neuroimaging: a validation study. Front Neurosci 8(229)Google Scholar
- 45.Gong X, Huang Y-X, Wang Y, Luo Y-J Revision of the Chinese facial affective picture system. Chin Ment Health J 2011Google Scholar
- 49.Witten IH, Frank E, Trigg LE, Hall MA, Holmes G, Cunningham SJ (1999) Weka: Practical machine learning tools and techniques with Java implementations.Google Scholar