Article

An EEG based pervasive depression detection for females

Authors:

Jing ChenAuthors Info & Claims

ICPCA/SWS'12: Proceedings of the 2012 international conference on Pervasive Computing and the Networked World

Pages 848 - 861

https://doi.org/10.1007/978-3-642-37015-1_74

Published: 28 November 2012 Publication History

Abstract

Recently, depression detection is mainly completed by some rating scales. This procedure requires attendance of physicians and the results may be more subjective. To meet emergent needs of objective and pervasive depression detection, we propose an EEG based approach for females. In the experiment, EEG of 13 depressed females and 12 age matched controls were collected in a resting state with eyes closed. Linear and nonlinear features extracted from artifact-free EEG epochs were subjected to statistical analysis to examine the significance of differences. Results showed that differences were significant for some EEG features between two groups (p<0.05) and the classification rates reached up to 92.9% and 94.2% with KNN and BPNN respectively. Our methods suggest that the discrimination of depressed females from controls is possible. We expect that our EEG based approach could be a pervasive assistant diagnosis tool for psychiatrists and health care specialists.

References

[1]

Wells, K. B., Stewart, A., Hays, R. D., Burnam, M. A., Rogers, W., Daniels, M., Berry, S., Greenfield, S., Ware, J.: The functioning and well-being of depressed patients. Results from the Medical Outcomes Study. JAMA 262, 914-919 (1989)

[2]

Kessler, R.C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., Rush, A. J., Walters, E. E., Wang, P. S.: The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA 289, 3095-3105 (2003)

[3]

Culbertson, F. M.: Depression and gender. An International Review. Am. Psychol. 52, 25-31 (1997)

[4]

Kessler, R.C.: Epidemiology of women and depression. J. Affect Disord. 74, 5-13 (2003)

[5]

Wetzel, J. W.: Depression: Women at risk. Social Work in Health Care 19, 85-108 (1994)

[6]

Michael, S., Carl, M., Pentl, Y.: Objective Physiological and Behavioral Measures for Identifying and Tracking Depression State in Clinically Depressed Patients. Massachusetts Institute of Technology Media Laboratory (2005)

[7]

Handte, M., Becker, C., Rothermel, K.: Peer-based automatic configuration of pervasive applications. In: Proceedings of International Conference on Pervasive Services, ICPS 2005, pp. 249-260 (2005)

[8]

Nunez, P. L., Srinivasan, R.: Electric Fields of the Brain: The Neurophysics of EEG. Oxford University Press (2006)

[9]

Thakor, N. V., Tong, S.: Advances in quantitative electroencephalogram analysis methods. Annu. Rev. Biomed. Eng. 6, 453-495 (2004)

[10]

Bin, H., Majoe, D., Ratcliffe, M., Yanbing, Q., Qinglin, Z., Hong, P., Dangping, F., Fang, Z., Jackson, M., Moore, P.: EEG-Based Cognitive Interfaces for Ubiquitous Applications: Developments and Challenges. IEEE Intelligent Systems 26, 46-53 (2011)

Digital Library

[11]

Kaneda, Y., Nakayama, H., Kagawa, K., Furuta, N., Ikuta, T.: Sex differences in visual evoked potential and electroencephalogram of healthy adults. Tokushima J. Exp. Med. 43, 143-157 (1996)

[12]

Corsi-Cabrera, M., Arce, C., Ramos, J., Guevara, M. A.: Effect of spatial ability and sex inter-and intrahemispheric correlation of EEG activity. Electroencephalography and Clinical Neurophysiology 102, 5-11 (1997)

[13]

Knott, V., Mahoney, C., Kennedy, S., Evans, K.: EEG power, frequency, asymmetry and coherence in male depression. Psychiatry Res. 106, 123-140 (2001)

[14]

Pollock, V. E., Schneidera, L. S.: Quantitative, waking EEG research on depression. Biological Psychiatry 27, 757-780 (1990)

[15]

Nuwer, M. R.: Quantitative EEG: II. Frequency analysis and topographic mapping in clinical settings. J. Clin. Neurophysiol. 5, 45-85 (1988)

[16]

Li, Y., Tong, S., Liu, D., Gai, Y., Wang, X., Wang, J., Qiu, Y., Zhu, Y.: Abnormal EEG complexity in patients with schizophrenia and depression. Clin. Neurophysiol. 119, 1232-1241 (2008)

[17]

Stam, C. J.: Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin. Neurophysiol. 116, 2266-2301 (2005)

[18]

Nandrino, J. L., Pezard, L., Martinerie, J.: Decrease of complexity in EEG as a symptom of depression. Neuroreport 5, 528-530 (1994)

[19]

Roschke, J., Fell, J., Beckmann, P.: Nonlinear analysis of sleep EEG in depression: calculation of the largest lyapunov exponent. Eur. Arch. Psychiatry Clin. Neurosci. 245, 27-35 (1995)

[20]

Foster, P. S., Yung, R.C., Branch, K. K., Stringer, K., Ferguson, B. J., Sullivan, W., Drago, V.: Increased spreading activation in depression. Brain Cogn. 77, 265-270 (2011)

[21]

Hong, P., Bin, H., Yanbing, Q., Qinglin, Z., Ratcliffe, M.: An improved EEG denoising approach in electroencephalogram (EEG) for home care. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 469-474 (2011)

[22]

Zhao, Q. L., Hu, B., Liu, L., Ratcliffe, M., Peng, H., Zhai, J. W., Li, L. L., Shi, Q.X., Liu, Q.Y., Qi, Y. B.: An EEG based nonlinearity analysis method for schizophrenia diagnosis. In: The Ninth IASTED International Conference on Biomedical Engineering (2012)

[23]

Grassberger, P., Procaccia, I.: Measuring the strangeness of strange attractors. Physica D: Nonlinear Phenomena 9, 189-208 (1983)

[24]

Rosenstein, M. T., Collins, J. J., De Luca, C. J.: A practical method for calculating largest Lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena 65, 117-134 (1993)

Digital Library

[25]

Lempel, A., Ziv, J.: On the Complexity of Finite Sequences. IEEE Transactions on Information Theory 22, 75-81 (1976)

Digital Library

[26]

Kaspar, F., Schuster, H. G.: Easily calculable measure for the complexity of spatiotemporal patterns. Phys. Rev. A 36, 842-848 (1987)

[27]

Fei-Yan, F., Ying-Jie, L., Yi-Hong, Q., Yi-Sheng, Z.: Use of ANN and Complexity Measures in Cognitive EEG Discrimination. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, pp. 4638-4641 (2005)

[28]

Holla, A. V. R., Aparna, P.: A nearest neighbor based approach for classifying epileptiform EEG using nonlinear DWT features. In: 2012 International Conference on Signal Processing and Communications (SPCOM), pp. 1-5 (2012)

[29]

Buchsbaum, M. S., Wu, J., DeLisi, L. E., Holcomb, H., Kessler, R., Johnson, J., King, A.C., Hazlett, E., Langston, K., Post, R. M.: Frontal cortex and basal ganglia metabolic rates assessed by positron emission tomography with {18F}2-deoxyglucose in affective illness. J. Affect Disord. 10, 137-152 (1986)

[30]

Nagata, K., Tagawa, K., Hiroi, S., Shishido, F., Uemura, K.: Electroencephalographic correlates of blood flow and oxygen metabolism provided by positron emission tomography in patients with cerebral infarction. Electroencephalogr. Clin. Neurophysiol. 72, 16-30 (1989)

[31]

Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H., Reiss, A. L., Schatzberg, A. F.: Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol. Psychiatry 62, 429-437 (2007)

[32]

Fingelkurts, A. A., Rytsala, H., Suominen, K., Isometsa, E., Kahkonen, S.: Impaired functional connectivity at EEG alpha and theta frequency bands in major depression. Human Brain Mapping 28, 247-261 (2007)

[33]

Na, S. H., Jin, S. H., Kim, S.Y., Ham, B. J.: EEG in schizophrenic patients: mutual information analysis. Clin. Neurophysiol. 113, 1954-1960 (2002)

[34]

Hosseinifard, B., Moradi, M. H., Rostami, R.: Classifying depression patients and normal subjects using machine learning techniques. In: 19th Iranian Conference on Electrical Engineering (ICEE), Tehran, pp. 1-4 (2011)

[35]

Gualtieri, C. T., Johnson, L. G., Benedict, K. B.: Neurocognition in depression: patients on and off medication versus healthy comparison subjects. J. Neuropsychiatry Clin. Neurosci. 18, 217-225 (2006)

Cited By

Qayyum ARazzak IMumtaz W(2020)Hybrid Deep Shallow Network for Assessment of Depression Using Electroencephalogram SignalsNeural Information Processing10.1007/978-3-030-63836-8_21(245-257)Online publication date: 18-Nov-2020
https://dl.acm.org/doi/10.1007/978-3-030-63836-8_21
Cai HWang ZZhang YChen YHu BZhang ZGu NLi SLu TLi L(2017)A Virtual-Reality Based Neurofeedback Game Framework for Depression Rehabilitation using Pervasive Three-Electrode EEG CollectorProceedings of the 12th Chinese Conference on Computer Supported Cooperative Work and Social Computing10.1145/3127404.3127433(173-176)Online publication date: 22-Sep-2017
https://dl.acm.org/doi/10.1145/3127404.3127433

An EEG based pervasive depression detection for females
1. Applied computing
  1. Life and medical sciences
  2. Operations research

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Published In

cover image Guide Proceedings

ICPCA/SWS'12: Proceedings of the 2012 international conference on Pervasive Computing and the Networked World

November 2012

917 pages

ISBN:9783642370144

Editors:
Qiaohong Zu
Wuhan University of Technology, Heping Road 1178, Wuchang District, Wuhan, Hubei, China
,
Bo Hu
Hayes Park Central, Fujitsu Laboratories of Europe Ltd., Hayes End Road, Hayes, Middlesex, UK
,
Atilla Elçi
Department of Electrical and Electronics Engineering, Aksaray University, Merkez Kampüsü, Aksaray, Middlesex, Turkey

Sponsors

ETH Zurich
CCF: China Computer Federation

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 28 November 2012

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Cited By

Qayyum ARazzak IMumtaz W(2020)Hybrid Deep Shallow Network for Assessment of Depression Using Electroencephalogram SignalsNeural Information Processing10.1007/978-3-030-63836-8_21(245-257)Online publication date: 18-Nov-2020
https://dl.acm.org/doi/10.1007/978-3-030-63836-8_21
Cai HWang ZZhang YChen YHu BZhang ZGu NLi SLu TLi L(2017)A Virtual-Reality Based Neurofeedback Game Framework for Depression Rehabilitation using Pervasive Three-Electrode EEG CollectorProceedings of the 12th Chinese Conference on Computer Supported Cooperative Work and Social Computing10.1145/3127404.3127433(173-176)Online publication date: 22-Sep-2017
https://dl.acm.org/doi/10.1145/3127404.3127433

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