Depression recognition using machine learning methods with different feature generation strategies - Li, Xiaowei; Zhang, Xin; Zhu, Jing; Mao, Wandeng; Sun, Shuting; Wang, Zihan; Xia, Chen; Hu, Bin (Artificial intelligence in medicine, 2019)
•Novel method for depression recognition of Electroencephalography is proposed.•Propose an ensemble model which consists of deep forest and SVM.•Add spatial information of EEG caps by image conversion method•Perform EEG signals processing and analysis on multiple frequency bands. The diagnosis of depression almost exclusively depends on doctor-patient communication and scale analysis, which have the obvious disadvantages such as patient denial, poor sensitivity, subjective biases and inaccuracy. An objective, automated method that predicts clinical outcomes in depression is essential for increasing the accuracy of depression recognition and treatments. This paper aims at better recognizing depression using the transformation of EEG features and machine learning methods. An experiment based on emotional face stimuli task was conducted, and twenty-eight subjects’ EEG data were recorded from 128-channel HydroCel Geodesic Sensor Net (HCGSN) by Net Station software. The Mini International Neuropsychiatric Interview (MINI) was used by psychiatrists as the criterion for diagnosis of depression patients. The power spectral density and activity were respectively extracted as original features using Auto-regress model and Hjorth algorithm with different time windows. Two separate approaches processed the features: ensemble learning and deep learning. For the ensemble learning, a deep forest transformed the original features to new features that potentially improve feature engineering and a support vector machine (SVM) that was applied as classifier. For deep learning method, we added spatial information of EEG caps to both features by image conversion and adopted convolutional neural network (CNN) to recognize them. The performance of both methods was evaluated for separated and total frequency bands. As a result, the best accuracy obtained was 89.02% when we used the ensemble model and power spectral density. The best accuracy of deep learning method was 84.75% using the activity. These experimental results prove the efficiency of the proposed methods and show that EEG could be used as a reliable indicator for depression recognition, which makes it possible for EEG-based portable system design and application in auxiliary depression recognition in the future.