Skip to main content
SpringerLink
Log in

Wearable EEG-Based Real-Time System for Depression Monitoring

  • Conference paper
  • First Online:
Brain Informatics (BI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10654))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Davidson, R.J., Pizzagalli, D., Nitschke, J.B., Putnam, K.: Depression: perspectives from affective neuroscience. Annu. Rev. Psychol. 53(1), 545–574 (2002)

    Article  Google Scholar 

  2. Kaplan, H.I., Sadock, B.J.: Comprehensive Textbook of Psychiatry. Williams & Wilkins, Baltimore (1980)

    Google Scholar 

  3. Bjelland, I., Dahl, A.A., Haug, T.T., Neckelmann, D.: The validity of the hospital anxiety and depression scale. an updated literature review. J. Psychosom. Res. 52(2), 69–77 (2002)

    Article  Google Scholar 

  4. Lupton, D., Jutel, A., Kawachi, I., Subramanian, S.V.: ‘It’s like having a physician in your pocket!’ a critical analysis of self-diagnosis smartphone apps. Soc. Sci. Med. 133, 128–135 (2015)

    Article  Google Scholar 

  5. Isais, R., Nguyen, K., Perez, G., Rubio, R.: A low-cost microcontroller-based wireless ECG-blood pressure telemonitor for home care. In: Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 4, pp. 3157–3160 (2003)

    Google Scholar 

  6. Thibodeau, R., Jorgensen, R.S., Kim, S.: Depression, anxiety, and resting frontal EEG asymmetry: a meta-analytic review. J. Abnorm. Psychol. 115(4), 715–729 (2006)

    Article  Google Scholar 

  7. Ziai, W.C., Dan, S., Llinas, R., Venkatesha, S., Truesdale, M., Schevchenko, A., Kaplan, P.W.: Emergent EEG in the emergency department in patients with altered mental states. Clin. Neurophysiol. 123(5), 910–917 (2012). Official Journal of the International Federation of Clinical Neurophysiology

    Article  Google Scholar 

  8. Ferree, T.C., Luu, P., Russell, G.S., Tucker, D.M.: Scalp electrode impedance, infection risk, and EEG data quality. Clin. Neurophysiol. 112(3), 536–44 (2001). Official Journal of the International Federation of Clinical Neurophysiology

    Article  Google Scholar 

  9. Liao, L.D., Chen, C.Y., Wang, I., Chen, S.F., Li, S.Y., Chen, B.W., Chang, J.Y., Lin, C.T.: Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors. J. Neuroeng. Rehabil. 9(1), 1–12 (2012)

    Article  Google Scholar 

  10. Lin, C.T., Chang, C.J., Lin, B.S., Hung, S.H.: A real-time wireless brain computer interface system for drowsiness detection. IEEE Trans. Biomed. Circ. Syst. 4(4), 214–222 (2010)

    Article  Google Scholar 

  11. Pantelopoulos, A., Bourbakis, N.G.: A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(1), 1–12 (2010)

    Article  Google Scholar 

  12. Lin, C.T., Chuang, C.H., Cao, Z., Singh, A., Huang, C.S., Yu, Y.H., Nascimben, M., Liu, Y.T., King, J.T., Su, T.P.: Forehead EEG in support of future feasible personal healthcare solutions: Sleep management, headache prevention, and depression treatment. IEEE Access PP(99), 1 (2017)

    Google Scholar 

  13. Davidson, R.J., Abercrombie, H., Nitschke, J.B., Putnam, K.: Regional brain function, emotion and disorders of emotion. Curr. Opin. Neurobiol. 9(2), 228–234 (1999)

    Article  Google Scholar 

  14. George, M.S., Ketter, T.A., Post, R.M.: Prefrontal cortex dysfunction in clinical depression. Depression 2(2), 59–72 (2010)

    Article  Google Scholar 

  15. Rajkowska, G., Miguelhidalgo, J.J.: Gliogenesis and glial pathology in depression. CNS Neurol. Disord. Drug Targets 6(3), 219 (2007)

    Article  Google Scholar 

  16. Kemp, A.H., Griffiths, K., Felmingham, K.L., Shankman, S.A., Drinkenburg, W., Arns, M., Clark, C.R., Bryant, R.A.: Disorder specificity despite comorbidity: resting eeg alpha asymmetry in major depressive disorder and post-traumatic stress disorder. Biol. Psychol. 85(2), 350–354 (2010)

    Article  Google Scholar 

  17. Hosseinifard, B., Moradi, M.H., Rostami, R.: Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Comput. Methods Programs Biomed. 109(3), 339 (2013)

    Article  Google Scholar 

  18. Bairy, G.M., Bhat, S., Eugene, L.W.J., Niranjan, U.C., Puthankattil, S.D., Joseph, P.K.: Automated classification of depression electroencephalographic signals using discrete cosine transform and nonlinear dynamics. J. Med. Imaging Health Inform. 5(3), 635–640 (2015)

    Article  Google Scholar 

  19. Zandi, A.S., Javidan, M., Dumont, G.A., Tafreshi, R.: Automated real-time epileptic seizure detection in scalp eeg recordings using an algorithm based on wavelet packet transform. IEEE Trans. Biomed. Eng. 57(7), 1639–51 (2010)

    Article  Google Scholar 

  20. Sourina, O., Wang, Q., Liu, Y., Nguyen, M.K.: A real-time fractal-based brain state recognition from EEG and its applications. In: Biosignals 2011 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing, Rome, Italy, 26–29 January, pp. 82–90 (2011)

    Google Scholar 

  21. Zotev, V., Phillips, R., Han, Y., Misaki, M., Bodurka, J.: Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback. Neuroimage 85(2), 985–995 (2014)

    Article  Google Scholar 

  22. Hu, B., Majoe, D., Ratcliffe, M., Qi, Y.: EEG-based cognitive interfaces for ubiquitous applications: developments and challenges. IEEE Intell. Syst. 26(5), 46–53 (2011)

    Article  Google Scholar 

  23. Bradley, M.M., Lang, P.J.: The international affective digitized sounds affective ratings of sounds and instruction manual. University of Florida (2007)

    Google Scholar 

  24. Pitas, I.: Nonlinear digital filters: principles and applications. Kluwer International (1990)

    Google Scholar 

  25. Wickert, M.: Modern digital signal processing. IEEE Trans. Educ. (2003). Special Issue on Circuits & Systems

    Google Scholar 

  26. Chen, Y., Zhao, Q., Hu, B., Li, J., Jiang, H., Lin, W., Li, Y., Zhou, S., Peng, H.: A method of removing ocular artifacts from EEG using discrete wavelet transform and Kalman filtering. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 1485–1492 (2016)

    Google Scholar 

  27. Jung, T.P., Humphries, C., Lee, T.W., Makeig, S., Mckeown, M.J., Iragui, V., Sejnowski, T.J.: Extended ICA removes artifacts from electroencephalographic recordings 10, 894–900 (1998)

    Google Scholar 

  28. Fang, C., Gu, F., Xu, J., Liu, Z., Ren, L.: A new measurement of complexity for studying EEG mutual information. In: Proceedings of the International Conference on Neural Information Processing, Iconip’r98, Kitakyushu, Japan, 21–23 October 1998, pp. 435–437 (1998)

    Google Scholar 

  29. Bandt, C., Pompe, B.: Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett. 88(17), 174102 (2002)

    Article  Google Scholar 

  30. Lempel, A., Ziv, J.: On the complexity of finite sequences. IEEE Trans. Inf. Theory 22(1), 75–81 (1976)

    Article  MATH  MathSciNet  Google Scholar 

  31. Bech, P., Kastrup, M., Rafaelsen, O.J.: Mini-compendium of rating scales for states of anxiety, depression, mania, schizophrenia with corresponding DSM-III syndromes. Acta Psychiatrica Scandinavica Supplementum 326, 1–37 (1985)

    Google Scholar 

  32. Kroenke, K., Spitzer, R.L.: The PHQ-9: a new depression diagnostic and severity measure. Psychiatr. Ann. 32(9), 509–521 (2002)

    Article  Google Scholar 

  33. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system, pp. 785–794 (2016)

    Google Scholar 

  34. Mao, C., Hu, B., Wang, M., Moore, P.: Learning from neighborhood for classification with local distribution characteristics. In: International Joint Conference on Neural Networks, pp. 1–8 (2015)

    Google Scholar 

  35. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Basic Research Program of China (973 Program) (No. 2014CB744600) and the National Natural Science Foundation of China (grant No. 61210010, No. 61632014, No. 61402211).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, China

    Shengjie Zhao, Qinglin Zhao, Xiaowei Zhang, Hong Peng, Zhijun Yao, Jian Shen, Yuan Yao, Hua Jiang & Bin Hu

Authors
  1. Shengjie Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qinglin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaowei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hong Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhijun Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jian Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yuan Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hua Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bin Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaowei Zhang .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Yi Zeng

  2. Beijing Normal University, Beijing, China

    Yong He

  3. KTH Royal Institute of Technology and Karolinska Institute, Stockholm, Sweden

    Jeanette Hellgren Kotaleski

  4. University of California, San Diego, San Diego, California, USA

    Maryann Martone

  5. Chinese Academy of Sciences, Beijing, China

    Bo Xu

  6. Allen Institute for Brain Science, Seattle, Washington, USA

    Hanchuan Peng

  7. Wuhan National Lab Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

    Qingming Luo

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, S. et al. (2017). Wearable EEG-Based Real-Time System for Depression Monitoring. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70772-3_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70771-6

  • Online ISBN: 978-3-319-70772-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation