Automated detection and screening of depression using continuous wavelet transform with electroencephalogram signals

Nearly 264 million people around the globe currently suffer from clinical depression, according to the World Health Organization. Although there are diagnostic techniques and treatments presently used by professionals, they are not always helpful. Herein, we suggest the use of advanced technological methods to diagnose depressed patients correctly. A machine learning approach is presented, which uses the electroencephalogram for diagnostics. The model extracts multiple features by applying a continuous wavelet transform (CWT) for each recording. These recordings are employed to train and test the model, with data gathered from 15 depressed and 15 normal patients. After the features are extracted from these recordings, it is organized into matrix form. The features are dimensionally reduced using kernel‐principal component analysis and principal component analysis techniques, ranked using Student's t‐test, and then labelled as normal or depressed with various classifiers. Accuracies of 99.33% and 99.13% were achieved for the right and left hemispheres of the brain, respectively, and 99.26% for the combined hemispheres of the brain. As compared to the discrete and empirical wavelet transform feature extraction methods, the CWT attained the best results. A depression severity index was also developed, using two features for discriminating the classes: normal versus depressed.

[1]  Sengul Dogan,et al.  Automated major depressive disorder detection using melamine pattern with EEG signals , 2021, Applied Intelligence.

[2]  U. Acharya,et al.  Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals , 2021, International journal of environmental research and public health.

[3]  Manish Sharma,et al.  Automatic identification of insomnia using optimal antisymmetric biorthogonal wavelet filter bank with ECG signals , 2021, Comput. Biol. Medicine.

[4]  U. Acharya,et al.  Automated detection of schizophrenia using optimal wavelet-based $$l_1$$ l 1 norm features extracted from single-channel EEG , 2021, Cognitive neurodynamics.

[5]  Joel Koh En Wei,et al.  Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images , 2021, Complex & Intelligent Systems.

[6]  U. Rajendra Acharya,et al.  Automated detection of shockable and non-shockable arrhythmia using novel wavelet-based ECG features , 2019, Comput. Biol. Medicine.

[7]  Hojjat Adeli,et al.  Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders , 2019, European Neurology.

[8]  Susanta Mukhopadhyay,et al.  Identifying twins based on ocular region features using deep representations , 2019, Applied Intelligence.

[9]  U. Rajendra Acharya,et al.  Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals , 2019, Journal of Medical Systems.

[10]  Fernando Blaya Haro,et al.  Design of a Functional Splint for Rehabilitation of Achilles Tendon Injury Using Advanced Manufacturing (AM) Techniques. Implementation Study , 2019, Journal of Medical Systems.

[11]  U. Rajendra Acharya,et al.  An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals , 2018, Cognitive Systems Research.

[12]  U. Rajendra Acharya,et al.  Analysis of knee-joint vibroarthographic signals using bandwidth-duration localized three-channel filter bank , 2018, Comput. Electr. Eng..

[13]  U. Rajendra Acharya,et al.  Automated EEG-based screening of depression using deep convolutional neural network , 2018, Comput. Methods Programs Biomed..

[14]  Oliver Faust,et al.  Automated diagnosis of depression electroencephalograph signals using linear prediction coding and higher order spectra features , 2017 .

[15]  Nitish V. Thakor,et al.  EEG Classification with a Sequential Decision-Making Method in Motor Imagery BCI , 2017, Int. J. Neural Syst..

[16]  Andreas Schulze-Bonhage,et al.  Physiological Ripples Associated with Sleep Spindles Differ in Waveform Morphology from Epileptic Ripples , 2017, Int. J. Neural Syst..

[17]  Tom Chau,et al.  Online EEG Classification of Covert Speech for Brain-Computer Interfacing , 2017, Int. J. Neural Syst..

[18]  Yi-Hung Liu,et al.  Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns , 2017, Sensors.

[19]  Qihua Wang,et al.  Distinct tribological mechanisms of various oxide nanoparticles added in PEEK composite reinforced with carbon fibers , 2017 .

[20]  Francesco Carlo Morabito,et al.  Permutation Disalignment Index as an Indirect, EEG-Based, Measure of Brain Connectivity in MCI and AD Patients , 2017, Int. J. Neural Syst..

[21]  Hongzhe Dai,et al.  A Wavelet Support Vector Machine‐Based Neural Network Metamodel for Structural Reliability Assessment , 2017, Comput. Aided Civ. Infrastructure Eng..

[22]  Francesco Carlo Morabito,et al.  Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia , 2017, Int. J. Neural Syst..

[23]  Francisco Sales,et al.  A Realistic Seizure Prediction Study Based on Multiclass SVM , 2017, Int. J. Neural Syst..

[24]  Juan Manuel Górriz,et al.  Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer's with Visual Support , 2017, Int. J. Neural Syst..

[25]  Sabine Van Huffel,et al.  An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation , 2017, Int. J. Neural Syst..

[26]  Klaus Lehnertz,et al.  Which Brain Regions are Important for Seizure Dynamics in Epileptic Networks? Influence of Link Identification and EEG Recording Montage on Node Centralities , 2017, Int. J. Neural Syst..

[27]  Ravishankar K. Iyer,et al.  Seizure Forecasting and the Preictal State in Canine Epilepsy , 2017, Int. J. Neural Syst..

[28]  Mehrdad Nourani,et al.  Multi-Biosignal Analysis for Epileptic Seizure Monitoring , 2017, Int. J. Neural Syst..

[29]  Qi Wu,et al.  Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere , 2016, Int. J. Neural Syst..

[30]  Fatma Latifoglu,et al.  Analysis of the Complexity Measures in the EEG of Schizophrenia Patients , 2016, Int. J. Neural Syst..

[31]  Danilo P. Mandic,et al.  Discriminating Multiple Emotional States from EEG Using a Data-Adaptive, Multiscale Information-Theoretic Approach , 2016, Int. J. Neural Syst..

[32]  H. Adeli,et al.  Brain functional connectivity patterns for emotional state classification in Parkinson’s disease patients without dementia , 2016, Behavioural Brain Research.

[33]  Hojjat Adeli,et al.  Computer-Aided Diagnosis of Parkinson’s Disease Using Enhanced Probabilistic Neural Network , 2015, Journal of Medical Systems.

[34]  Dariusz Komorowski,et al.  The Use of Continuous Wavelet Transform Based on the Fast Fourier Transform in the Analysis of Multi-channel Electrogastrography Recordings , 2015, Journal of Medical Systems.

[35]  Joel E. W. Koh,et al.  A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals , 2015, European Neurology.

[36]  Joel E. W. Koh,et al.  Computer-Aided Diagnosis of Depression Using EEG Signals , 2015, European Neurology.

[37]  Francesco Carlo Morabito,et al.  A Longitudinal EEG Study of Alzheimer's Disease Progression Based on A Complex Network Approach , 2015, Int. J. Neural Syst..

[38]  Oliver Faust,et al.  DEPRESSION DIAGNOSIS SUPPORT SYSTEM BASED ON EEG SIGNAL ENTROPIES , 2014 .

[39]  U. Rajendra Acharya,et al.  ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform , 2013, Biomed. Signal Process. Control..

[40]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[41]  H. Adeli,et al.  Spatiotemporal Analysis of Relative Convergence of EEGs Reveals Differences Between Brain Dynamics of Depressive Women and Men , 2013, Clinical EEG and neuroscience.

[42]  Subha D. Puthankattil,et al.  CLASSIFICATION OF EEG SIGNALS IN NORMAL AND DEPRESSION CONDITIONS BY ANN USING RWE AND SIGNAL ENTROPY , 2012 .

[43]  H. Adeli,et al.  Fractality analysis of frontal brain in major depressive disorder. , 2012, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[44]  Reza Rostami,et al.  Classifying depression patients and normal subjects using machine learning techniques , 2011, 2011 19th Iranian Conference on Electrical Engineering.

[45]  Hojjat Adeli,et al.  Enhanced probabilistic neural network with local decision circles: A robust classifier , 2010, Integr. Comput. Aided Eng..

[46]  H. Adeli,et al.  Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology , 2010 .

[47]  Lipo Wang,et al.  A Modified T-test Feature Selection Method and Its Application on the HapMap Genotype Data , 2008, Genom. Proteom. Bioinform..

[48]  Hojjat Adeli,et al.  Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection , 2008, IEEE Transactions on Biomedical Engineering.

[49]  Hojjat Adeli,et al.  Wavelet Packet‐Autocorrelation Function Method for Traffic Flow Pattern Analysis , 2004 .

[50]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[51]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[52]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[53]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[54]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[55]  Manish Sharma,et al.  Automated identification of insomnia using optimal bi-orthogonal wavelet transform technique with single-channel EEG signals , 2021, Knowl. Based Syst..

[56]  Aamir Saeed Malik,et al.  Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD) , 2017, Biomed. Signal Process. Control..

[57]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.