Abstract
EEG signals are non-stationary, complex and non-linear signals. During major depressive disorder (MDD) or depression, any deterioration in the brain function is reflected in the EEG signals. In this paper, linear features (band power, inter hemispheric asymmetry) and non-linear features [relative wavelet energy (RWE) and wavelet entropy (WE)] and combination of linear and non-linear features were used to classify depression patients and healthy individuals. In this analysis the data set used is publicly available data set contributed by Mumtaz et al. (Biomed Signal Process Control 31:108–115, 2017b). The dataset consisted of 34 MDD patients and 30 healthy individuals. The classifiers used were multi layered perceptron neural network (MLPNN), radial basis function network (RBFN), linear discriminant analysis (LDA) and quadratic discriminant analysis. When linear feature was used, highest classification accuracy of 91.67% was obtained by alpha power with MLPNN classifier. When non-linear feature was used, both RWE and WE provided highest classification accuracy of 90% with RBFN and LDA classifier, respectively. The highest classification of 93.33% was achieved when combining linear and non-linear feature, i.e., combination alpha power and RWE with MLPNN as well as RBFN classifier. This paper also showed that the combination of non-linear features, i.e., RWE and WE also performed the best with highest classification accuracy of 93.33%. The study compared the accuracy, sensitivity and specificity of different classifiers along with linear and non-linear features and combination of both. The results indicated that combination alpha power and RWE showed the highest classification 93.33% accuracy in all the applied classifiers.
Similar content being viewed by others
References
American Psychiatric Association (1994) Diagnostic and statistical manual of mental disorders, 4th edn. American Psychiatric Association, Washington, DC, pp 339–345
Bachmann M, Lass J, Suhhova A, Hinrikus H (2013) Spectral asymmetry and Higuchi’s fractal dimension measures of depression electroencephalogram. Comput Math Methods Med 2013:1–9
Bachmann M, Lass J, Hinrikus H (2017) Single channel EEG analysis for detection of depression. Biomed Signal Process Control 31:391–397
Bishop C (2006) Linear models for classification. In: Jordan M, Kleinberg J, Scholkopf B (eds) Pattern recognition and machine learning. Springer, Singapore, pp 186–189
Bopardikar AS, Rao RM (1998) Wavelet transforms: Introduction to Theory and Applications. Dorling Kindersley Publishing Inc, New Delhi, pp 2–82
Bruder GE, Stewart JW, Hellerstein D et al (2012) Abnormal functional brain asymmetry in depression: evidence of biologic commonality between major depression and dysthymia. Psychiatry Res 196:250–254
Cusin C, Yang H, Yeung A et al (2009) Rating scales for depression. In: Baer L, Blais MA (eds) Handbook of clinical rating scales and assessment in psychiatry and mental health. Current Clinical Psychiatry, Boston, pp 7–37
Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134:9–21
Dharmadhikari AS, Tandle AL, Jaiswal SV et al (2018) Frontal theta asymmetry as a biomarker of depression. East Asian Arch Psychiatry 28:17–22
Dien J (1998) Issues in the application of the average reference: review, critiques and recommendations. Behav Res Methods Instrum Comput 30:34–43
Gandhi V (2014) Brain computer interfacing for assistive robotics. Electroencephalograms, recurrent quantum neural networks, and user-centric graphical interfaces, 1st edn. Academic Press, Cambridge, pp 21–29
Gollan JK, Hoxha D, Chihade D et al (2014) Frontal alpha EEG asymmetry before and after behavioral activation treatment for depression. Biol Psychol 99:198–208
Grin-Yatsenko VA, Baas I, Ponomarev VA et al (2010) Independent component approach to the analysis of EEG recordings at early stages of depressive disorders. Clin Neurophysiol 281:281–289
Haykin S (2009) Multilayer perceptrons. In: Dworkin A, Mars D, Opaluc W (eds) Neural networks and learning machines, 3rd edn. Pearson Education, Cranbury, pp 1–263
Hinrikus H, Sukhova A, Bachmann M et al (2009) Electroencephalographic spectral asymmetry index for detection of depression. Med Biomed Eng Comput 47:1291–1299
Hosseinifarda B, Moradia MH, Rostami R (2013) Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Comput Methods Programs Biomed 109:339–345
James G (2013) Classification. In: Casella G, Fienberg S, Olkin I (eds) An introduction to statistical learning with applications in R. Springer, New York, pp 138–150
Jolliffe IT (2002) Principal component analysis, series: Springer series in statistics, 2nd edn. Springer, New York, pp 1–147
Joyce CA, Gorodnitsky IF, Kutas M (2003) Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. In: Fabiani M, Jennings JR (eds) Psychophysiology. Blackwell Publishing Inc, Malden, pp 313–325
Jung TP, Humphries C, Lee TW et al (1998) Extended ICA removes artifacts from electroencephalographic recordings. Adv Neural Inf Process Syst 10:1–7
Jung TP, Makeig S, Humphries C et al (2000) Removing electroencephalographic artifacts by blind source separation. Psychophysiology. Cambridge University Press, Cambridge, pp 163–178
Mallat SG (1989) A theory for multi-resolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell II:674–694
Mohammadi M, Al-Azab F, Raahem B et al (2015) Data mining EEG signals in depression for their diagnostic value. BMC Med Inform Decis Making 108:108–123
Mumtaz W, Xia L, Ali SSA et al (2017a) A wavelet-based technique to predict treatment outcome for major depressive disorder. PLoS One 2017:1–30
Mumtaz W, Xia L, Ali SSA et al (2017b) Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD). Biomed Signal Process Control 31:108–115
Puthankattil SD, Joseph PK (2012) Classification of EEG signals In normal and depression conditions by ANN using RWE and signal entropy. J Mech Med Biol 12:1240019–1240032
Puthankattil SD, Joseph PK (2014) Analysis of EEG signals using wavelet entropy and approximate entropy: a case study on depression patients. Int J Bioeng Life Sci 8:420–424
Ricardo-Garcel J, Gonzalez-Olvera JJ, Miranda E et al (2010) EEG sources in a group of patients with major depressive disorders. Int J Psychophysiol 71:70–74
Rodreguez-Bermudez G, Garcia-Laencina P (2015) Analysis of EEG signals using nonlinear dynamics and chaos: a review. Appl Math Inf Sci 9:2309–2321
Rosso OA, Martin MT, Figliola A et al (2006) EEG analysis using wavelet-based information tools. J Neurosci Methods 153:163–182
Stewart JL, Coan JA, Towers DN et al (2014) Resting and task-elicited prefrontal EEG alpha asymmetry in depression: support for the capability model. Psychophysiology 51:1–18
Tharwat A (2016) Linear vs. quadratic discriminant analysis classifier: a tutorial. Int J Appl. Pattern Recognit 3(2):145–180
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mahato, S., Paul, S. Detection of major depressive disorder using linear and non-linear features from EEG signals. Microsyst Technol 25, 1065–1076 (2019). https://doi.org/10.1007/s00542-018-4075-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00542-018-4075-z