A wavelet-based technique to predict treatment outcome for Major Depressive Disorder.
Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant's treatment outcome may help during antidepressant's selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant's treatment outcome for the MDD patients.
Centre for Intelligent Signal and Imaging Research (CISIR),Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia.
Wajid
http://orcid.org/0000-0001-6468-8666
W
Mumtaz
Beijing Institute of Technology, Beijing, China.
Likun
L
Xia
Department of Psychiatry,Universiti Sains Malaysia, Jalan Hospital Universiti Sains Malaysia, Kubang Kerian, Kota Bharu, Kelantan, Malaysia.
Mohd Azhar
MA
Mohd Yasin
Centre for Intelligent Signal and Imaging Research (CISIR),Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia.
Syed Saad
SS
Azhar Ali
Centre for Intelligent Signal and Imaging Research (CISIR),Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia.
Aamir Saeed
AS
Malik
D000328:Adult; D000928:Antidepressive Agents / Q000627:therapeutic use; D001921:Brain / Q000503:physiopathology; D003865:Major Depressive Disorder / Q000188:drug therapy* / Q000503:physiopathology; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D011237:Predictive Value of Tests; D017367:Selective Serotonin Reuptake Inhibitors / Q000627:therapeutic use; D016896:Treatment Outcome
D000928:Antidepressive Agents; D017367:Selective Serotonin Reuptake Inhibitors