Wearable EEG-Based Real-Time System for Depression Monitoring

It has been reported that depression can be detected by electrophysiological signals. However, few studies investigate how to daily monitor patient’s electrophysiological signals through a more convenient way for a doctor, especially on the monitoring of electroencephalogram (EEG) signals for depression diagnosis. Since a person’s mental state and physiological state are changing over time, the most insured diagnosis of depression requires doctors to collect and analyze subject’s EEG signals every day until two weeks for the clinical practice. In this work, we designed a real-time depression monitoring system to capture the user’s EEG data by a wearable device and to perform real-time signal filtering, artifacts removal and power spectrum visualization, which could be combined with psychological test scales as an auxiliary diagnosis. In addition to collecting the resting EEG signals for real-time analysis or diagnosis of depression, we also introduced an external audio stimulus paradigm to further make a detection of depression. Through the machine learning method, system can give a credible probability of depression under each stimulus as a user’s self-rating score from continuous EEG data. EEG signals collected from 81 early-onset patients and 89 normal controls are used to build the final classification model and to verify the practical performance.

[1]  Nikolaos G. Bourbakis,et al.  A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Bin Hu,et al.  Learning from neighborhood for classification with local distribution characteristics , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[3]  Fang Chen,et al.  A New Measurement of Complexity for Studying EEG Mutual Information , 1998, ICONIP.

[4]  Han Yuan,et al.  Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback , 2013, NeuroImage.

[5]  Bin Hu,et al.  A method of removing Ocular Artifacts from EEG using Discrete Wavelet Transform and Kalman Filtering , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[6]  Shreya Bhat,et al.  Automated Classification of Depression Electroencephalographic Signals Using Discrete Cosine Transform and Nonlinear Dynamics , 2015 .

[7]  Ioannis Pitas,et al.  Nonlinear Digital Filters - Principles and Applications , 1990, The Springer International Series in Engineering and Computer Science.

[8]  R. Davidson,et al.  Depression: perspectives from affective neuroscience. , 2002, Annual review of psychology.

[9]  Reza Tafreshi,et al.  Automated Real-Time Epileptic Seizure Detection in Scalp EEG Recordings Using an Algorithm Based on Wavelet Packet Transform , 2010, IEEE Transactions on Biomedical Engineering.

[10]  Tzyy-Ping Jung,et al.  Extended ICA Removes Artifacts from Electroencephalographic Recordings , 1997, NIPS.

[11]  Roberto Cristi,et al.  Modern Digital Signal Processing , 2003 .

[12]  Reza Rostami,et al.  Classifying depression patients and normal subjects using machine learning techniques , 2011, 2011 19th Iranian Conference on Electrical Engineering.

[13]  R. A. Bryant,et al.  Disorder specificity despite comorbidity: Resting EEG alpha asymmetry in major depressive disorder and post-traumatic stress disorder , 2010, Biological Psychology.

[14]  Deborah Lupton,et al.  'It's like having a physician in your pocket!' A critical analysis of self-diagnosis smartphone apps. , 2015, Social science & medicine.

[15]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[16]  Ashley N. Johnson,et al.  Dual-task motor performance with a tongue-operated assistive technology compared with hand operations , 2012, Journal of NeuroEngineering and Rehabilitation.

[17]  D. Tucker,et al.  Scalp electrode impedance, infection risk, and EEG data quality , 2001, Clinical Neurophysiology.

[18]  R. Isais,et al.  A low-cost microcontroller-based wireless ECG-blood pressure telemonitor for home care , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[19]  P Bech,et al.  Mini-compendium of rating scales for states of anxiety depression mania schizophrenia with corresponding DSM-III syndromes. , 1986, Acta psychiatrica Scandinavica. Supplementum.

[20]  Qiang Wang,et al.  A Real-time Fractal-based Brain State Recognition from EEG and its Applications , 2011, BIOSIGNALS.

[21]  Richard J Davidson,et al.  Regional brain function, emotion and disorders of emotion , 1999, Current Opinion in Neurobiology.

[22]  Bin Hu,et al.  EEG-Based Cognitive Interfaces for Ubiquitous Applications: Developments and Challenges , 2011, IEEE Intelligent Systems.

[23]  Wendy C. Ziai,et al.  Emergent EEG in the emergency department in patients with altered mental states , 2012, Clinical Neurophysiology.

[24]  Jyh-Yeong Chang,et al.  Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors , 2012, Journal of NeuroEngineering and Rehabilitation.

[25]  R. Post,et al.  Prefrontal cortex dysfunction in clinical depression , 1994 .

[26]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[27]  Chin-Teng Lin,et al.  A Real-Time Wireless Brain–Computer Interface System for Drowsiness Detection , 2010, IEEE Transactions on Biomedical Circuits and Systems.

[28]  G. Rajkowska,et al.  Gliogenesis and glial pathology in depression. , 2007, CNS & neurological disorders drug targets.

[29]  R. Spitzer,et al.  The PHQ-9: A new depression diagnostic and severity measure , 2002 .

[30]  Abraham Lempel,et al.  On the Complexity of Finite Sequences , 1976, IEEE Trans. Inf. Theory.

[31]  Chun-Hsiang Chuang,et al.  Forehead EEG in Support of Future Feasible Personal Healthcare Solutions: Sleep Management, Headache Prevention, and Depression Treatment , 2017, IEEE Access.

[32]  Sangmoon Kim,et al.  Depression, anxiety, and resting frontal EEG asymmetry: a meta-analytic review. , 2006, Journal of abnormal psychology.

[33]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[34]  T. T. Haug,et al.  The validity of the Hospital Anxiety and Depression Scale. An updated literature review. , 2002, Journal of psychosomatic research.