Abstract
Depression is a common mental disorder with growing prevalence; however current diagnoses of depression face the problem of patient denial, clinical experience and subjective biases from self-report. By using a combination of linear and nonlinear EEG features in our research, we aim to develop a more accurate and objective approach to depression detection that supports the process of diagnosis and assists the monitoring of risk factors. By classifying EEG features during free viewing task, an accuracy of 99.1 %, which is the highest to our knowledge by far, was achieved using kNN classifier to discriminate depressed and non-depressed subjects. Furthermore, through correlation analysis, comparisons of performance on each electrode were discussed on the availability of single channel EEG recording depression detection system. Combined with wearable EEG collecting devices, our method offers the possibility of cost effective wearable ubiquitous system for doctors to monitor their patients with depression, and for normal people to understand their mental states in time.
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Acknowledgments
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.60973138, grant No.61003240), the International Cooperation Project of Ministry of Science and Technology (No.2013DFA11140), the National Basic Research Program of China (973 Program) (No.2011CB711000).
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This article is part of the Topical Collection on Systems-Level Quality Improvement.
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Li, X., Hu, B., Shen, J. et al. Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks. J Med Syst 39, 187 (2015). https://doi.org/10.1007/s10916-015-0345-9
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DOI: https://doi.org/10.1007/s10916-015-0345-9