A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

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Global Epileptic Seizure Identification With Affinity Propagation Clustering Partition Mutual Information Using Cross-Layer Fully Connected Neural Network

P2860

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P2860

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

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A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

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A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

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type

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label

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

@ast

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

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A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

@ast

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

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P1476

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

@en

P2093

Aamir Saeed Malik

http://www.w3.org/2001/XMLSchema#string

Likun Xia

http://www.w3.org/2001/XMLSchema#string

Mohd Azhar Mohd Yasin

http://www.w3.org/2001/XMLSchema#string

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The Comorbidity of Major Depression and Anxiety Disorders: Recognition and Management in Primary Care

Early changes in prefrontal activity characterize clinical responders to antidepressants

Early reduction in prefrontal theta QEEG cordance value predicts response to venlafaxine treatment in patients with resistant depressive disorder

The change of prefrontal QEEG theta cordance as a predictor of response to bupropion treatment in patients who had failed to respond to previous antidepressant treatments

Are antidepressant drugs that combine serotonergic and noradrenergic mechanisms of action more effective than the selective serotonin reuptake inhibitors in treating major depressive disorder? A meta-analysis of studies of newer agents

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice

Rostral anterior cingulate cortex theta current density and response to antidepressants and placebo in major depression

Validity and reliability of electroencephalographic frontal alpha asymmetry and frontal midline theta as biomarkers for depression.

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder.

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e0171409

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scholarly article

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10.1371/JOURNAL.PONE.0171409

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English

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http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

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Wajid Mumtaz

Syed Saad Azhar Ali

P577

2017-02-02T00:00:00Z

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28152063

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P932

5289714

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A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

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P2860

P2860

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

description

name

type

label

prefLabel

P1433

P1476

P2093

P2860

P304

P31

P356

P407

P433

P478

P50

P577

P698

P932

P356

P1433

P1476

P2093

P2860

P304

P31

P356

P407

P433

P478

P50

P577

P698

P932