Abstract
Depression or Major Depressive Disorder (MDD) is a psychiatric disorder. It is the major contributor to overall global burden of disease. Any deterioration in brain functioning is reflected in Electroencephalogram (EEG) signals. EEG signals are highly complex, nonstationary and nonlinear. So, it is very difficult to analyze visually and identify changes in the waveform in order to classify MDD and normal subjects. Thus, computerized quantitative EEG is used for the analysis of signals. Support Vector Machine (SVM) using band power feature reported an accuracy of 98.33% and using Kernel Eigen-Filter-Bank Common Spatial Patterns (KEFB-CSP) gave an accuracy of 91.67% [1, 2]. Logistic Regression (LR) using band power feature reported an accuracy of 98.33%, using 4 nonlinear features combination provided an accuracy of 90%, using wavelet transform provided an accuracy of 87.5% and using only alpha power gave an accuracy of 73.3% [1, 3, 4]. Naïve Bayesian (NB) using band power feature provided an accuracy of 96.8% [1]. Artificial Neural Network using Relative Wavelet Energy (RWE) reported an accuracy of 98.11%, using power spectrum feature gave accuracy of 84% and using Lep–Ziv complexity accuracy of 60–80% was reported [5, 6, 7]. Linear Discriminant Analysis (LDA) reported an accuracy of 91.2% using SASI (Spectral Asymmetry Index) and DFA (Detrended Fluctuation Analysis) [8]. Decision Tree provided an accuracy of 80% using EEG band power as feature [9]. The study reveals that, in general, high classification accuracy is achieved by SVM, LR and ANN and highest classification accuracy of 98.33% is achieved by SVM. Highest accuracy is achieved by SVM because it is more robust and computationally more efficient due to maximal margin gap between separating hyper planes and kernel trick. The study gives some ideas which could be helpful for guiding and improving future researches. Since linear and nonlinear method for feature extraction are both equally efficient. So any of the linear/nonlinear feature can be used. For feature selection and reduction, Genetic Algorithm (GA), Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) can be used. Since SVM, LR and ANN give high classification accuracy and any of them can be used or any hybrid technique like GA-SVM, GA-ANN can be used. This chapter compares various EEG signal analysis techniques, compares their accuracy and methodology used and finally recommends the most suitable technique based on the accuracy for detection of depression. The chapter also summarizes some of the key finding related to EEG features based on present state of art. These results could be helpful for guiding and improving the future research in this area.
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References
Li Y, Fan F (2005) Classification of schizophrenia and depression by EEG with ANNs. In: 27th annual international conference of engineering in medicine and biology society, IEEE-EMB, 2005, pp 1–6
Stewart JL, Coan JA, Towers DN, Allen JJB (2014) Resting and task-elicited prefrontal EEG alpha asymmetry in depression: support for the capability model. Psychophysiology, 446–455
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
Mantri S, Agrawal P, Patil D, Wadhai V (2015) Non invasive EEG signal processing framework for real time depression analysis. In: SAI intelligent systems conference, pp 518–521
Fan F, Li Y, Qiu Y, Zhu Y (2005) Use of ANN and complexity measures in cognitive EEG discrimination. In: Engineering in medicine and biology society, 27th annual international conference, IEEE-EMBS, 2005, pp 1–6
Liao S, Wu C, Huang H, Cheng W, Liu Y (2017) Major depression detection from EEG signals using kernel eigen-filter-bank common spatial patterns. Sensors, pp 1–14
Hinrikus H, Suhhova A, Bachmann M, Aadamsoo K, Vohma U, Lass J, Tuulik V (2009) Electroencephalographic spectral asymmetry index for detection of depression. Med Biomed Eng Comput, pp 1291–1299
Mumtaz W, Xia L, Ali SSA, Yasin MMAM, Hussain M, Malik AS (2017) Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD). Biomed Signal Process Control 31:108–115
World Health Organization (2017) Depression and other common mental disorders global health estimates. WHO Document Production Services, Geneva, Switzerland
World Health Organization (2011) Global burden of mental disorders and the need for a comprehensive, coordinated response from health and social sectors at the country level. Report by the Secretariat. EB 130/9
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 2013:1–8
Grin-Yatsenko VA, Baas I, Ponomarev VA, Kropotov JD (2010) Independent component approach to the analysis of EEG recordings at early stages of depressive disorders. Clin Neurophysiol 121:281–289
Mohammadi M et al (2015) Data mining EEG signals in depression for their diagnostic value. BMC Med Inf Decis Mak, pp 108–123
Bruder GE, Stewart JW, Hellerstein D, Alvarenga JE, Alschuler D, McGratha PJ (2012) Abnormal functional brain asymmetry in depression: evidence of biologic commonality between major depression and dysthymia. Psychiatry Res, pp 250–254
Bjork MH, Sand T, Bråthen G, Linaker OM, Morken G, Nilsen BM, Vaaler AE (2008) Quantitative EEG findings in patients with acute, brief depression combined with other fluctuating psychiatric symptoms: a controlled study from an acute psychiatric department. BMC Psychiatry, 2008, pp 1–6
Ricardo-Garcell J (2009) EEG sources in a group of patients with major depressive disorders. Int J Psychophysiol 71:70–74
Bachmann M, Lass J, Hinrikus H (2017) Single channel EEG analysis for detection of depression. Biomed Signal Process Control 31:391–397
Subasi A (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32:1084–1093
Mumtaz W, Xia L, Yasin MAM, Ali SSA, Malik AS (2016) A wavelet-based technique to predict treatment outcome for major depressive disorder. PLoS ONE, pp 1–6
Li Y, Li Y, Tong S, Tang Y, Zhu Y (2007) More normal EEGs of depression patients during mental arithmetic than rest. In: Joint meeting of the 6th international symposium on noninvasive functional source imaging of the brain and heart and the international conference on functional biomedical imaging, 2007, pp 165–168
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
Sood M, Bhooshan SV (2014) Automatic processing of EEG signals for Seizure detection using soft computing techniques. In: IEEE international conference on recent advances and innovations in engineering, 2014, pp 1–6
Chandaka S, Chatterjee A, Munshi S (2009) Cross-correlation aided support vector machine classifier for classification of EEG signals. Expert Syst Appl 36:1329–1336
Subasi A, Gursoy MI (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37:8569–8666
Sabetia M, Boostani R, Katebi SD, Price GW (2007) Selection of relevant features for EEG signal classification of schizophrenic patients. Biomed Signal Process Control 2:122–134
Kalaivani M, Kalaivani V, Devi AV (2014) Analysis of EEG signal for the detection of brain abnormalities. In: IJCA proceedings on international conference on simulations in computing nexus pp 1–6
Kumar RSS, Jose JP (2011) Seizure detection in EEG using time frequency analysis and SVM. In: International conference on emerging trends in electrical and computer technology (ICETECT), IEEE, pp 1–6
Kousarrizi MRN (2009) Feature extraction and classification of EEG signals using wavelet transform, SVM and artificial neural networks for brain computer interfaces. In: International joint conference on bioinformatics, systems biology and intelligent computing, IEEE, 2009, pp 352–355
Tzallas AT, Tsipouras MG, Fotiadis DI (2007) The use of time-frequency distributions for epileptic seizure detection in EEG recordings. In: Proceedings of the 29th annual international conference of the IEEE EMBS, 2007, pp 3–6
Abásolo D, Hornero R, Escudero J, Gomez C, Garcia M, Lopez M (2006) Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients. In: IET 3rd international conference on advances in medical, signal and information processing, IEEE, 2006, pp 1–6
Tsoi C, So DSC, Sergejew A (1993) Classification of electroencephalogram using artificial neural networks. In: 7th NIPS conference conference: advances in neural information processing systems 6, 1993, pp 1151–1158
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Mahato, S., Paul, S. (2019). Electroencephalogram (EEG) Signal Analysis for Diagnosis of Major Depressive Disorder (MDD): A Review. In: Nath, V., Mandal, J. (eds) Nanoelectronics, Circuits and Communication Systems . Lecture Notes in Electrical Engineering, vol 511. Springer, Singapore. https://doi.org/10.1007/978-981-13-0776-8_30
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