Depression recognition using machine learning methods with different feature generation strategies

Publication date: Available online 17 July 2019Source: Artificial Intelligence in MedicineAuthor(s): Xiaowei Li, Xin Zhang, Jing Zhu, Wandeng Mao, Shuting Sun, Zihan Wang, Chen Xia, Bin HuAbstractThe 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...

Source: Artificial Intelligence in Medicine - July 18, 2019 Category: Bioinformatics Source Type: research