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Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance
(2015-01-12)
ObjectiveaaThe combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for ...
ARTIFICIAL INTELLIGENCE APPROACH TO CLASSIFY UNIPOLAR and BIPOLAR DEPRESSIVE DISORDERS
(2015)
Machine learning (ML) approaches for medical decision making processes are valuable when both
high classification accuracy and less feature requirements are satisfied. Artificial neural networks ...
Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification
(Psychiatry Investigation, 2014)
Objective:Many applications such as biomedical signals require selecting a subset of the input features in order to represent the whole set of features. A feature selection algorithm has recently been proposed as a new ...
Analysis of Brain Functional Changes in High-Frequency Repetitive Transcranial Magnetic Stimulation in Treatment-Resistant Depression
(Clinical EEG & Neuroscience, 2014)
Repetitive transcranial magnetic stimulation (rTMS) is a treatment procedure that uses magnetic fields to stimulate nerve cells in
the brain, and is associated with significant improvements in clinical symptoms of major ...
A Hybrid artificial intelligence method to classify trichotillomania and obsessive compulsive disorder
(2015-02)
Classification of psychiatric disorders is becoming one of the major focuses of research using artificial
intelligence approach. The combination of feature selection and classification methods generates
satisfactory ...
Discriminating schizophrenia and schizo-obsessive disorder: a structural MRI study combining VBM and machine learning methods
(2016-07-12)
Schizo-obsessive disorder is characterized by the
clinical syndrome in which comorbid obsessive–compulsive
disorder accompanies schizophrenia. A substantial
number of studies have investigated the neuropsychological
and ...