Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder.

Predicting the therapeutic result of repetitive transcranial magnetic stimulation (rTMS) treatment could save time and costs as ineffective treatment can be avoided. To this end, we presented a machine-learning-based strategy for classifying patients with major depression disorder (MDD) into responders (R) and nonresponders (NR) to rTMS treatment. Resting state EEG data were recorded using 32 electrodes from 88 MDD patients before treatment.

Simultaneous electroencephalography-functional magnetic resonance imaging for assessment of human brain function.

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (MRI) have long been used as tools to examine brain activity. Since both methods are very sensitive to changes of synaptic activity, simultaneous recording of EEG and fMRI can provide both high temporal and spatial resolution. Therefore, the two modalities are now integrated into a hybrid tool, EEG-fMRI, which encapsulates the useful properties of the two.

Localizing Epileptic Foci Using Simultaneous EEG-fMRI Recording: Template Component Cross-Correlation.

Conventional EEG-fMRI methods have been proven to be of limited use in the sense that they cannot reveal the information existing in between the spikes. To resolve this issue, the current study obtains the epileptic components time series detected on EEG and uses them to fit the Generalized Linear Model (GLM), as a substitution for classical regressors. This approach allows for a more precise localization, and equally importantly, the prediction of the future behavior of the epileptic generators.

Localizing confined epileptic foci in patients with an unclear focus or presumed multifocality using a component-based EEG-fMRI method.

Precise localization of epileptic foci is an unavoidable prerequisite in epilepsy surgery. Simultaneous EEG-fMRI recording has recently created new horizons to locate foci in patients with epilepsy and, in comparison with single-modality methods, has yielded more promising results although it is still subject to limitations such as lack of access to information between interictal events. This study assesses its potential added value in the presurgical evaluation of patients with complex source localization.

Quantitative determination of concordance in localizing epileptic focus by component-based EEG-fMRI.

Background And Objective: Accurate seizure onset zone (SOZ) localization is an essential step in pre-surgical assessment of patients with refractory focal epilepsy. Complex pathophysiology of epileptic cerebral structures, seizure types and frequencies have not been considered as influential features for accurate identification of SOZ using EEG-fMRI. There is a crucial need to quantitatively measure concordance between presumed SOZ and IED-related BOLD response in different brain regions to improve SOZ delineation.

An optimal strategy for prediction of sudden cardiac death through a pioneering feature-selection approach from HRV signal.

Background And Objective: Taking into consideration the critical importance of Sudden cardiac death (SCD), as it could be the first and the last heart condition to be diagnosed in a person while continuing to claim millions of lives around the world, prediction of sudden cardiac death has increasingly been regarded as a matter of substantive significance. This study does not seek to once again define features to predict and detect SCD, as there already has been adequate discussion addressing feature extraction in our previous works and other recent studies. What seems to be lacking attention is the need for an appropriate strategy to manage the extracted features to such an extent that the best classification is presented.