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2021 | OriginalPaper | Chapter

Prediction of Depression Using EEG: A Comparative Study

Authors : Namrata P. Mohanty, Sweta Shree Dash, Sandeep Sobhan, Tripti Swarnkar

Published in: Progress in Advanced Computing and Intelligent Engineering

Publisher: Springer Singapore

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Abstract

The worldwide havoc of today’s world: depression, is increasing in this era. Depression is not any specific disease rather the determinant factor in the onset of numerous terrible diseases. With the increase in automation and artificial intelligence, it has become easier to predict depression before a much earlier time. The machine learning techniques are used in the classification of EEG for the prediction of different neuro-problems. EEG signals are the brain waves which can easily detect any abnormalities occurring in the brain waves, thereby making it easier to predict the seizure formation or depression. Proposed work uses the EEG signals for the analysis of brain waves, thereby predicting depression. In this paper, we have compared two widely used benchmark models, i.e., the k-NN and the ANN for the prediction of depression with an accuracy of 85%. This method will help doctors and medical associates in predicting diseases before the onset of its extreme phase, as well as assist them in providing the best treatments, possible in proper time.

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European Data Format (EDF). http://www.edfplus.info

MathWorks—MATLAB and Simulink for Technical Computing. https://www.mathworks.com

Mallikarjun, H.M., Dr. Suresh, H.N.: Depression level prediction using EEG signals processing. In: International Conference on Contemporary Computing and Informatics (IC31), pp. 928–933 (2014)

Biosemi EEG ECG EMG BSPM NEURO amplifiers systems. http://www.biosemi.com/faq/file_format.htm

https://www.eecs.qmul.ac.uk/mmv/datasets/deap/download.html

Khan, N.A., Jönsson, P., Sandsten, M., Performance comparison of time-frequency distributions for estimation of instantaneous frequency of heart rate variability signals. Appl. Sci. 7(3), 221 (2017). https://doi.org/10.3390/app7030221

Gautam, R., Mrs. Shimi S.L.: Features extraction and depression level prediction by using EEG signals. Int. Res. J. Eng. Technol. (IRJET) 04(05) (2017)

Ebrahimi, F., Mikaeili, M., Estrada, E., Nazeran, H.: Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients. In: 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada, August 20–24, 2008, pp. 1151–1154. https://doi.org/10.1109/iembs.2008.4649365

Knott, Verner., Mahoney, Colleen., Kennedy, Sidney, Evans, Kenneth: EEG power, frequency, asymmetry and coherence in male depression. Psych. Res. Neuroimaging Sect. 106, 123–140 (2001)CrossRef

10.

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

11.

Shih-Cheng Liao, Chien-Te Wu, Hao-Chuan Huang, Wei-Teng Cheng, Yi-Hung Liu, ” Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns”, Sensors (Basel) 2017 Jun; 17(6): 1385. Published online 2017 Jun 14. 10.3390/s17061385

12.

Chisci, L., Mavino, A., Perferi, G., Sciandrone, M., Anile, C., Colicchio, G., Fuggetta, F.: Real-time epileptic seizure prediction using AR models and support vector machines. IEEE Trans. Biomed. Eng. 57(5), 1124–32 (2010). https://doi.org/10.1109/TBME.2009.2038990. Epub 2010 Feb 17CrossRef

13.

Karim, H.T., Wang, M., Andreescu, C., Tudorascu, D., Butters, M.A., Karp, J.F., Reynolds, C.F., 3rd Aizenstein, H.J.: Acute trajectories of neural activation predict remission to pharmacotherapy in late-life depression. Neuroimage Clin 8(19), 831–839 (2018). https://doi.org/10.1016/j.nicl.2018.06.006

14.

Kwon, Oh-Young, Park, Sung-Pa: Depression and anxiety in people with epilepsy. J Clin Neurol. 10(3), 175–188 (2014). https://doi.org/10.3988/jcn.2014.10.3.175CrossRef

15.

Acharya, U.R., Hagiwara, Y., Adeli, H.: Automated seizure prediction. Epilepsy Behav. 88, 251–261 (2018). https://doi.org/10.1016/j.yebeh.2018.09.030. Epub 2018 Oct 11CrossRef

16.

Varatharajah, Y., Iyer, R.K., Berry, B.M., Worrell, G.A., Brinkmann, B.H.: Seizure forecasting and the preictal state in canine epilepsy. Int. J. Neural Syst. 27:1650046 (2017) [12 pp.]

17.

Günay, M., Ensari, T.: EEG signal analysis of patients with epilepsy disorder using machine learning techniques. In: 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), Istanbul, pp. 1–4 (2018)

18.

Kumar, P.N., Kareemullah, H.: EEG signal with feature extraction using SVM and ICA classifiers. In: International Conference on Information Communication and Embedded Systems (ICICES2014), Chennai, pp. 1–7 (2014). https://doi.org/10.1109/icices.2014.7034090

19.

Mirowski, P., Madhavan, D., LeCun, Y., Kuzniecky, R.: Classification of patterns of EEG synchronization for seizure prediction. Clin. Neurophysiol. 120(11), 1927–1940 (2009)

20.

Jauhar, S., Krishnadas, R., Nour, M.M., Cunningham-Owens, D., Johnstone, E.C., Lawrie, S.M.: Is there a symptomatic distinction between the affective psychoses and schizophrenia? A machine learning approach. Schizophr. Res. 202, 241–247 (2018). https://doi.org/10.1016/j.schres.2018.06.070CrossRef

21.

Dipnall, J.F., Pasco, J.A., Berk, M., Williams, L.J., Dodd, S., Jacka, F.N., Meyer, D.: Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM). Eur. Psych. 39, 40–50 (2017). https://doi.org/10.1016/j.eurpsy.2016.06.003CrossRef

22.

Liu, A. et al.: Machine learning aided prediction of family history of depression. In: 2017 New York Scientific Data Summit (NYSDS), New York, NY, pp. 1–4 (2017).https://doi.org/10.1109/nysds.2017.8085046

23.

Sri, K.S., Rajapakse, J.C.: Extracting EEG rhythms using ICA-R. In: IEEE International Joint Conference on Neural Networks, IJCNN 2008. (IEEE World Congress on Computational Intelligence), pp. 2133–2138 (2008)

24.

Malmivuo, J., Plonsey, R.: Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields. Oxford University Press (1995)

25.

Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004)CrossRef

26.

Wu, Y., Ianakiev, K., Govindaraju, V.: Improved k-nearest neighbor classification. Pattern Recogn. 35(10), 2311–2318 (2002)CrossRef

27.

Ho, C.K., Sasaki, M.: EEG data classification with several mental tasks. In: 2002 IEEE International Conference on Systems, Man and Cybernetics, vol. 6, p. 4 (2002)

28.

About GSIL—Blekinge Institute of Technology—in real life. http://www.bth.se/com/gsil. Accessed 05 August 2012

Title: Prediction of Depression Using EEG: A Comparative Study
Authors: Namrata P. Mohanty
Sweta Shree Dash
Sandeep Sobhan
Tripti Swarnkar
Publisher: Springer Singapore
Book: Progress in Advanced Computing and Intelligent Engineering
Print ISBN: 978-981-15-6352-2

Electronic ISBN: 978-981-15-6353-9

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-981-15-6353-9_1

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