Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Mumtaz, Wajid, Xia, Likun, Mohd Yasin, Mohd Azhar, Azhar Ali, Syed Saad, Malik, Aamir Saeed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5289714/
https://www.ncbi.nlm.nih.gov/pubmed/28152063
http://dx.doi.org/10.1371/journal.pone.0171409
_version_ 1782504541451714560
author Mumtaz, Wajid
Xia, Likun
Mohd Yasin, Mohd Azhar
Azhar Ali, Syed Saad
Malik, Aamir Saeed
author_facet Mumtaz, Wajid
Xia, Likun
Mohd Yasin, Mohd Azhar
Azhar Ali, Syed Saad
Malik, Aamir Saeed
author_sort Mumtaz, Wajid
collection PubMed
description 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.
format Online
Article
Text
id pubmed-5289714
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-52897142017-02-17 A wavelet-based technique to predict treatment outcome for Major Depressive Disorder Mumtaz, Wajid Xia, Likun Mohd Yasin, Mohd Azhar Azhar Ali, Syed Saad Malik, Aamir Saeed PLoS One Research Article 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. Public Library of Science 2017-02-02 /pmc/articles/PMC5289714/ /pubmed/28152063 http://dx.doi.org/10.1371/journal.pone.0171409 Text en © 2017 Mumtaz et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mumtaz, Wajid
Xia, Likun
Mohd Yasin, Mohd Azhar
Azhar Ali, Syed Saad
Malik, Aamir Saeed
A wavelet-based technique to predict treatment outcome for Major Depressive Disorder
title A wavelet-based technique to predict treatment outcome for Major Depressive Disorder
title_full A wavelet-based technique to predict treatment outcome for Major Depressive Disorder
title_fullStr A wavelet-based technique to predict treatment outcome for Major Depressive Disorder
title_full_unstemmed A wavelet-based technique to predict treatment outcome for Major Depressive Disorder
title_short A wavelet-based technique to predict treatment outcome for Major Depressive Disorder
title_sort wavelet-based technique to predict treatment outcome for major depressive disorder
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5289714/
https://www.ncbi.nlm.nih.gov/pubmed/28152063
http://dx.doi.org/10.1371/journal.pone.0171409
work_keys_str_mv AT mumtazwajid awaveletbasedtechniquetopredicttreatmentoutcomeformajordepressivedisorder
AT xialikun awaveletbasedtechniquetopredicttreatmentoutcomeformajordepressivedisorder
AT mohdyasinmohdazhar awaveletbasedtechniquetopredicttreatmentoutcomeformajordepressivedisorder
AT azharalisyedsaad awaveletbasedtechniquetopredicttreatmentoutcomeformajordepressivedisorder
AT malikaamirsaeed awaveletbasedtechniquetopredicttreatmentoutcomeformajordepressivedisorder
AT mumtazwajid waveletbasedtechniquetopredicttreatmentoutcomeformajordepressivedisorder
AT xialikun waveletbasedtechniquetopredicttreatmentoutcomeformajordepressivedisorder
AT mohdyasinmohdazhar waveletbasedtechniquetopredicttreatmentoutcomeformajordepressivedisorder
AT azharalisyedsaad waveletbasedtechniquetopredicttreatmentoutcomeformajordepressivedisorder
AT malikaamirsaeed waveletbasedtechniquetopredicttreatmentoutcomeformajordepressivedisorder