“School of Cognitive”

Back to Papers Home
Back to Papers of School of Cognitive

Paper   IPM / Cognitive / 16876
School of Cognitive Sciences
  Title:   PREDICTING CLINICAL RESPONSE TO TRANSCRANIAL MAGNETIC STIMULATION IN MAJOR DEPRESSION USING TIME-FREQUENCY EEG SIGNAL PROCESSING
  Author(s): 
1.  E. Ebrahimzadeh
2.  M. Asgarinejad
3.  S. Saliminia
4.  S. Ashoori
5.  M. Seraji
  Status:   Published
  Journal: Biomedical Engineering
  No.:  06
  Vol.:  33
  Year:  2021
  Supported by:  IPM
  Abstract:
Repetitive transcranial magnetic stimulation (rTMS) is defined as a noninvasive technique of brain stimulation conducted for both diagnostic and therapeutic purposes. rTMS can effectively excite the brain neurons and increase brain plasticity, which becomes particularly useful in psychiatric and neurological fields. Biomarkers that predict clinical outcomes in depression are essential for increasing the precision of treatments and clinical outcomes. The electroencephalogram (EEG) is a noninvasive neurophysiological test that is promising as a biomarker sensitive to treatment effects. The aim of our study was to investigate a novel nonlinear index of the resting state EEG activity as a predictor of clinical outcome and compare its predictive capacity to traditional frequency-based indices. EEG was recorded from 50 patients with treatment resistant depression (TRD) and 24 healthy comparison (HC) subjects. TRD patients were treated with excitatory rTMS to the dorsolateral prefrontal cortex (DLPFC) for 4–6 weeks. EEG signals were first decomposed using the ICA algorithm and the extracted components were then processed by time-frequency analysis. We then go on to compare the participants’ depression severity before, after, and 2 months after finishing the last treatment session using the proposed rTMS therapy. Absolute powers (APs), band powers (BPs), and theta and beta band entropies (BAs), which were extracted from the EEG, are used as features for the classification of changes in patients and normal cases after applying rTMS. Accordingly, we can go beyond the Beck score and clinically classify the EEG signal into two classes: depression and normal. The results demonstrated 78.37

Download TeX format
back to top
scroll left or right