Investigation Capability of EEG-Based Non-Linear Features in Depression Detection
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Abstract
Purpose: The purpose of this study is to investigate the potential of non-linear electroencephalography-based features in depression detection.
Materials and Methods: First, the Electroencephalography (EEG) signal was recorded from 25 normal and 25 depressed subjects. After preprocessing these signals, non-linear features including the Sum of Logarithmic (SL) and second-order spectral Moment (2M) of the amplitudes of diagonal elements in the bispectrum, the normalized Entropy (En) of bispectrum in beta and gamma frequency bands, Katz Fractal Dimension (KFD), and Lempel-Ziv Complexity (LZC) are extracted from them. Then, the ability of these features in depression detection was investigated using Mann-Whitney statistical test. Also, the classification performance of significant features was evaluated using a support vector machine (SVM) classifier.
Results: The results of the statistical analysis demonstrate that bispectral 2M, SL, and KFD features show significant differences between depressed and healthy groups in the Eyes-Closed (EC) condition. Also, bispectral 2M and SL in the gamma frequency band show significant differences between the two groups in parietal and temporal regions in the EC condition and only in the temporal region in the Eyes-Open (EO) condition. Bispectral En does not show a significant difference in the whole 19 channel, but it shows significant differences in the frontal region and beta frequency band. Between these features, gamma bispectral 2M in the temporal region and EO condition shows the highest classification result with 78.6±7.2% accuracy.
Conclusion: Findings confirm that bispectral 2M in the gamma frequency band and EO condition can classify depression and healthy subjects.
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[29] F. Hasanzadeh, M. Mohebbi, and R. Rostami, "Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal," Journal of affective disorders, vol. 256, pp. 132-142, 2019.
[30] A. Shalbaf, S. Bagherzadeh, and A. Maghsoudi, "Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals," Physical and Engineering Sciences in Medicine, vol. 43, no. 4, pp. 1229-1239, 2020.
[31] B. Richhariya and M. Tanveer, "EEG signal classification using universum support vector machine," Expert Systems with Applications, vol. 106, pp. 169-182, 2018.
[32] V. Aayesh, M. Dehghani, M. Torabi, S. Akhtari-Khosroshahi, S. Gharibzadeh, "Prediction mini-mental state examination scores using electroencephalography signal features," Frontires in Biomedical Technologies, vol. 9, no. 4, pp. 274-282, 2022.
[33] W. S. Noble, "What is a support vector machine?," Nature biotechnology, vol. 24, no. 12, pp. 1565-1567, 2006.
[34] H. Hinrikus et al., "Spectral features of EEG in depression," 2010.
[35] Y. Sun, Y. Li, Y. Zhu, X. Chen, and S. Tong, "Electroencephalographic differences between depressed and control subjects: an aspect of interdependence analysis," Brain Research Bulletin, vol. 76, no. 6, pp. 559-564, 2008.
[36] H. Cai, X. Sha, X. Han, S. Wei, and B. Hu, "Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector," in 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2016, pp. 1239-1246: IEEE.
[37] J. Shen, S. Zhao, Y. Yao, Y. Wang, and L. Feng, "A novel depression detection method based on pervasive EEG and EEG splitting criterion," in 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017, pp. 1879-1886: IEEE.
[38] F. S. de Aguiar Neto and J. L. G. Rosa, "Depression biomarkers using non-invasive EEG: a review," Neuroscience & Biobehavioral Reviews, vol. 105, pp. 83-93, 2019.
[39] P. J. Fitzgerald and B. O. Watson, "Gamma oscillations as a biomarker for major depression: an emerging topic," Translational psychiatry, vol. 8, no. 1, pp. 1-7, 2018.
[40] W. Heller, M. A. Etienne, and G. A. Miller, "Patterns of perceptual asymmetry in depression and anxiety: implications for neuropsychological models of emotion and psychopathology," Journal of abnormal psychology, vol. 104, no. 2, p. 327, 1995.
[41] K. Kalev, M. Bachmann, L. Orgo, J. Lass, and H. Hinrikus, "Lempel-Ziv and multiscale Lempel-Ziv complexity in depression," in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 4158-4161: IEEE.
[2] H. Cai et al., "A pervasive approach to EEG-based depression detection," Complexity, vol. 2018, 2018.
[3] W. Depression, "Other common mental disorders: global health estimates," Geneva: World Health Organization, vol. 24, 2017.
[4] M. G. Mazza et al., "Anxiety and depression in COVID-19 survivors: Role of inflammatory and clinical predictors," Brain, behavior, and immunity, vol. 89, pp. 594-600, 2020.
[5] M. Bachmann et al., "Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis," Computer methods and programs in biomedicine, vol. 155, pp. 11-17, 2018.
[6] E. Avots, K. Jermakovs, M. Bachmann, L. Päeske, C. Ozcinar, and G. Anbarjafari, "Ensemble approach for detection of depression using EEG features," Entropy, vol. 24, no. 2, p. 211, 2022.
[7] D. K. Hannesdóttir, J. Doxie, M. A. Bell, T. H. Ollendick, and C. D. Wolfe, "A longitudinal study of emotion regulation and anxiety in middle childhood: Associations with frontal EEG asymmetry in early childhood," Developmental Psychobiology: The Journal of the International Society for Developmental Psychobiology, vol. 52, no. 2, pp. 197-204, 2010.
[8] S. R. Sponheim, B. A. Clementz, W. G. Iacono, and M. Beiser, "Clinical and biological concomitants of resting state EEG power abnormalities in schizophrenia," Biological psychiatry, vol. 48, no. 11, pp. 1088-1097, 2000.
[9] R. Thibodeau, R. S. Jorgensen, and S. Kim, "Depression, anxiety, and resting frontal EEG asymmetry: a meta-analytic review," Journal of abnormal psychology, vol. 115, no. 4, p. 715, 2006.
[10] B. Ay et al., "Automated depression detection using deep representation and sequence learning with EEG signals," Journal of medical systems, vol. 43, no. 7, pp. 1-12, 2019.
[11] S. D. Puthankattil and P. K. Joseph, "Classification of EEG signals in normal and depression conditions by ANN using RWE and signal entropy," Journal of Mechanics in Medicine and biology, vol. 12, no. 04, p. 1240019, 2012.
[12] W. Mumtaz, L. Xia, S. S. A. Ali, M. A. M. Yasin, M. Hussain, and A. S. Malik, "Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD)," Biomedical Signal Processing and Control, vol. 31, pp. 108-115, 2017.
[13] M. Ahmadlou, H. Adeli, and A. Adeli, "Fractality analysis of frontal brain in major depressive disorder," International Journal of Psychophysiology, vol. 85, no. 2, pp. 206-211, 2012.
[14] B. Hosseinifard, M. H. Moradi, and R. Rostami, "Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal," Computer methods and programs in biomedicine, vol. 109, no. 3, pp. 339-345, 2013.
[15] O. Faust, P. C. A. Ang, S. D. Puthankattil, and P. K. Joseph, "Depression diagnosis support system based on EEG signal entropies," Journal of mechanics in medicine and biology, vol. 14, no. 03, p. 1450035, 2014.
[16] G. M. Bairy, S. Bhat, L. W. J. Eugene, U. Niranjan, S. D. Puthankattil, and P. K. Joseph, "Automated classification of depression electroencephalographic signals using discrete cosine transform and nonlinear dynamics," Journal of Medical Imaging and Health Informatics, vol. 5, no. 3, pp. 635-640, 2015.
[17] M. Cukic, D. Pokrajac, M. Stokic, V. Radivojevic, and M. Ljubisavljevic, "EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression," arXiv preprint arXiv:1803.05985, 2018.
[18] A. Saeedi, M. Saeedi, A. Maghsoudi, and A. Shalbaf, "Major depressive disorder diagnosis based on effective connectivity in EEG signals: A convolutional neural network and long short-term memory approach," Cognitive Neurodynamics, vol. 15, no. 2, pp. 239-252, 2021.
[19] P. D. Purnamasari, A. A. P. Ratna, and B. Kusumoputro, "Development of filtered bispectrum for EEG signal feature extraction in automatic emotion recognition using artificial neural networks," Algorithms, vol. 10, no. 2, p. 63, 2017.
[20] M. Mohebbi and H. Ghassemian, "Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal," Computer methods and programs in biomedicine, vol. 105, no. 1, pp. 40-49, 2012.
[21] T.-P. Jung et al., "Removing electroencephalographic artifacts by blind source separation," Psychophysiology, vol. 37, no. 2, pp. 163-178, 2000.
[22] H. Nolan, R. Whelan, and R. B. Reilly, "FASTER: fully automated statistical thresholding for EEG artifact rejection," Journal of neuroscience methods, vol. 192, no. 1, pp. 152-162, 2010.
[23] A. J. Ibáñez-Molina, S. Iglesias-Parro, M. F. Soriano, and J. I. Aznarte, "Multiscale Lempel–Ziv complexity for EEG measures," Clinical Neurophysiology, vol. 126, no. 3, pp. 541-548, 2015.
[24] A. N. Kolmogorov, "Three approaches to the quantitative definition ofinformation'," Problems of information transmission, vol. 1, no. 1, pp. 1-7, 1965.
[25] A. Lempel and J. Ziv, "On the complexity of finite sequences," IEEE Transactions on information theory, vol. 22, no. 1, pp. 75-81, 1976.
[26] J. E. Jacob, G. K. Nair, A. Cherian, and T. Iype, "Application of fractal dimension for EEG based diagnosis of encephalopathy," Analog Integrated Circuits and Signal Processing, vol. 100, no. 2, pp. 429-436, 2019.
[27] S. Dorosti and R. Khosrowabadi, "Fractal dimension of EEG signal senses complexity of fractal animations," bioRxiv, 2021.
[28] E. Bou Assi, L. Gagliano, S. Rihana, D. K. Nguyen, and M. Sawan, "Bispectrum features and multilayer perceptron classifier to enhance seizure prediction," Scientific reports, vol. 8, no. 1, pp. 1-8, 2018.
[29] F. Hasanzadeh, M. Mohebbi, and R. Rostami, "Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal," Journal of affective disorders, vol. 256, pp. 132-142, 2019.
[30] A. Shalbaf, S. Bagherzadeh, and A. Maghsoudi, "Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals," Physical and Engineering Sciences in Medicine, vol. 43, no. 4, pp. 1229-1239, 2020.
[31] B. Richhariya and M. Tanveer, "EEG signal classification using universum support vector machine," Expert Systems with Applications, vol. 106, pp. 169-182, 2018.
[32] V. Aayesh, M. Dehghani, M. Torabi, S. Akhtari-Khosroshahi, S. Gharibzadeh, "Prediction mini-mental state examination scores using electroencephalography signal features," Frontires in Biomedical Technologies, vol. 9, no. 4, pp. 274-282, 2022.
[33] W. S. Noble, "What is a support vector machine?," Nature biotechnology, vol. 24, no. 12, pp. 1565-1567, 2006.
[34] H. Hinrikus et al., "Spectral features of EEG in depression," 2010.
[35] Y. Sun, Y. Li, Y. Zhu, X. Chen, and S. Tong, "Electroencephalographic differences between depressed and control subjects: an aspect of interdependence analysis," Brain Research Bulletin, vol. 76, no. 6, pp. 559-564, 2008.
[36] H. Cai, X. Sha, X. Han, S. Wei, and B. Hu, "Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector," in 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2016, pp. 1239-1246: IEEE.
[37] J. Shen, S. Zhao, Y. Yao, Y. Wang, and L. Feng, "A novel depression detection method based on pervasive EEG and EEG splitting criterion," in 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017, pp. 1879-1886: IEEE.
[38] F. S. de Aguiar Neto and J. L. G. Rosa, "Depression biomarkers using non-invasive EEG: a review," Neuroscience & Biobehavioral Reviews, vol. 105, pp. 83-93, 2019.
[39] P. J. Fitzgerald and B. O. Watson, "Gamma oscillations as a biomarker for major depression: an emerging topic," Translational psychiatry, vol. 8, no. 1, pp. 1-7, 2018.
[40] W. Heller, M. A. Etienne, and G. A. Miller, "Patterns of perceptual asymmetry in depression and anxiety: implications for neuropsychological models of emotion and psychopathology," Journal of abnormal psychology, vol. 104, no. 2, p. 327, 1995.
[41] K. Kalev, M. Bachmann, L. Orgo, J. Lass, and H. Hinrikus, "Lempel-Ziv and multiscale Lempel-Ziv complexity in depression," in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 4158-4161: IEEE.
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| Issue | Vol 12 No 4 (2025) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v12i4.19809 | |
| Keywords | ||
| Depression Diagnosis Electroencephalography Bispectrum Katz Fractal Dimension Lempel-Ziv Complexity Classification | ||
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How to Cite
1.
Dehghani M, Asayesh V, Torabi Nikjeh M, Akhtari Khosroshahi S. Investigation Capability of EEG-Based Non-Linear Features in Depression Detection. Frontiers Biomed Technol. 2025;12(4):722-733.
