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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. Then, patients underwent 7 weeks of rTMS, and 46 of them responded to treatment. By applying Independent Component Analysis (ICA) on EEG, we identified the relevant brain sources as possible indicators of neural activity in the dorsolateral prefrontal cortex (DLPFC). This was served through estimating the generators of activity in the sensor domain. Subsequently, we added physiological information and placed certain terms and conditions to offer a far more realistic estimation than the classic EEG. Ultimately, those components mapped in accordance with the region of the DLPFC in the sensor domain were chosen. Features extracted from the relevant ICs time series included permutation entropy (PE), fractal dimension (FD), Lempel-Ziv Complexity (LZC), power spectral density, correlation dimension (CD), features based on bispectrum, frontal and prefrontal cordance, and a combination of them. The most relevant features were selected by a Genetic Algorithm (GA). For classifying two groups of R and NR, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) were applied to predict rTMS treatment response. To evaluate the performance of classifiers, a 10-fold cross-validation method was employed. A statistical test was used to assess the capability of features in differentiating R and NR for further research. EEG characteristics that can predict rTMS treatment response were discovered. The strongest discriminative indicators were EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands, and CD. The Combined feature vector classified R and NR with a high performance of 94.31% accuracy, 92.85% specificity, 95.65% sensitivity, and 92.85% precision using SVM. This result indicates that our proposed method with power and nonlinear and bispectral features from relevant ICs time-series can predict the treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording. The obtained results show that the proposed method outperforms previous methods.

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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. Among other benefits, EEG-fMRI can contribute to a better understanding of brain connectivity and networks. This review lays its focus on the methodologies applied in performing EEG-fMRI studies, namely techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. We will investigate simultaneous resting-state and task-based EEG-fMRI studies and discuss their clinical and technological perspectives. Moreover, it is established that the brain regions affected by a task-based neural activity might not be limited to the regions in which they have been initiated. Advanced methods can help reveal the regions responsible for or affected by a developed neural network. Therefore, we have also looked into studies related to characterization of structure and dynamics of brain networks. The reviewed literature suggests that EEG-fMRI can provide valuable complementary information about brain neural networks and functions.

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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. Adult candidates considered ineligible for surgery on account of an unclear focus and/or presumed multifocality on the basis of EEG underwent EEG-fMRI. Adopting a component-based approach, this study attempts to identify the neural behavior of the epileptic generators and detect the components-of-interest which will later be used as input in the GLM model, substituting the classical linear regressor. Twenty-eight sets interictal epileptiform discharges (IED) from nine patients were analyzed. In eight patients, at least one BOLD response was significant, positive and topographically related to the IEDs. These patients were rejected for surgery because of an unclear focus in four, presumed multifocality in three, and a combination of the two conditions in two. Component-based EEG-fMRI improved localization in five out of six patients with unclear foci. In patients with presumed multifocality, component-based EEG-fMRI advocated one of the foci in five patients and confirmed multifocality in one of the patients. In seven patients, component-based EEG-fMRI opened new prospects for surgery and in two of these patients, intracranial EEG supported the EEG-fMRI results. In these complex cases, component-based EEG-fMRI either improved source localization or corroborated a negative decision regarding surgical candidacy. As supported by the statistical findings, the developed EEG-fMRI method leads to a more realistic estimation of localization compared to the conventional EEG-fMRI approach, making it a tool of high value in pre-surgical evaluation of patients with refractory epilepsy. To ensure proper implementation, we have included guidelines for the application of component-based EEG-fMRI in clinical practice.

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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. METHODS: A novel component-based EEG-fMRI approach is proposed to measure physical distance between BOLD clusters and selected component dipole location using patient-specific high resolution anatomical images. The method is applied on 18 patients with refractory focal epilepsy to localize epileptic focus and determine concordance quantitatively and compare between maximum BOLD cluster with identified component dipole. To measure concordance, distance from a voxel with maximal z-score of maximum BOLD to center of extracted component dipole is measured. RESULTS: BOLD clusters to spikes distances for concordant (<25 mm), partially concordant (25-50 mm), and discordant (>50 mm) groups were significantly different (p < 0.0001). The results showed full concordance in 17 IED types (17.85 ±â€¯4.69 mm), partial concordance in 4 (36.47 ± 8.84 mm), and nodiscordance, which is a significant rise compared to the existing literature. The proposed method is premised on the cross-correlation between the spike template outside the scanner and the highly-ranked extracted components. It successfully surpasses the limitations of conventional EEG-fMRI studies which are largely dependent on inside-scanner spikes. More significantly, the proposed method improves localization accuracy to 97% which marks a dramatic rise compared to conventional works. CONCLUSIONS: This study demonstrated that BOLD changes were related to epileptic spikes in different brain regions in patients with refractory focal epilepsy. In a systematic quantitative approach, concordance levels based on the distance between center of maximum BOLD cluster and dipole were determined by component-based EEG-fMRI method. Therefore, component-based EEG-fMRI can be considered as a reliable predictor of SOZ in patients with focal epilepsy and included as part of clinical evaluation for patients with medically resistant epilepsy.

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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. To this end, deploying a suitable tactic to select extracted features could bring about outstanding results compared to other works in the literature. METHODS: This research has accordingly applied a novel and automated approach to Local Feature Subset Selection through the most rigorous methodologies, which have formerly been developed in previous works of this team, for extracting features from nonlinear, time-frequency and classical processes. We are therefore enabled to select features that differ from one another in each minute before the incident through the agency of optimal feature selection in each one-minute interval of the signal. RESULTS: Using the proposed algorithm, SCD can be predicted 13 min before the onset, thus, better results are achieved compared to other techniques proposed in this field. Additionally, through defining a Utility Function and employing statistical analysis the alarm threshold has been effectively determined as 84% for the prediction accuracy. Having selected the best combination of features, based on their ability to generate the highest classification accuracy, the two classes are classified by means of the Multilayer Perceptron (MLP), K- Nearest Neighbor (KNN) the Support Vector Machine (SVM), and the Mixture of Expert (ME) classifier. The Mixture of Experts classification yielded more precise class discrimination than other well-known classifiers. The performance of the proposed method was evaluated using the MIT-BIH database which led to sensitivity, specificity, and accuracy of 84.24%, 85.71%, and 82.85%, respectively for thirteen one-minute. CONCLUSIONS: The outcome of the obtained prediction would be analyzed and compared to other results. The most applicable and effective features would subsequently be presented according to the number of times they have been chosen. Finally, principal features in each time interval are discussed and the importance of each type of processing will be drawn into focus. The results indicate the significant capacity of the proposed method for predicting SCD from Electrocardoigram (ECG) signals as well as selecting the appropriate processing method at any time before the incident.

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BACKGROUND AND OBJECTIVE: Paroxysmal Atrial Fibrillation (PAF) is one of the most common major cardiac arrhythmia. Unless treated timely, PAF might transform into permanent Atrial Fibrillation leading to a high rate of morbidity and mortality. Therefore, increasing attention has been directed towards prediction of PAF, to enable early detection and prevent further progression of the disease. Notwithstanding the pharmacological and electrical treatments, a validated method to predict the onset of PAF is yet to be developed. We aim to address this issue through integrating classical and modern methods. METHODS: To increase the predictivity, we have made use of a combination of features extracted through linear, time-frequency, and nonlinear analyses performed on heart rate variability. We then apply a novel approach to local feature selection using meticulous methodologies, developed in our previous works, to reduce the dimensionality of the feature space. Subsequently, the Mixture of Experts classification is employed to ensure a precise decision-making on the output of different processes. In the current study, we analyzed 106 signals from 53 pairs of ECG recordings obtained from the standard database called Atrial Fibrillation Prediction Database (AFPDB). Each pair of data contains one 30-min ECG segment that ends just before the onset of PAF event and another 30-min ECG segment at least 45 min distant from the onset. RESULTS: Combining the features that are extracted using both classical and modern analyses was found to be significantly more effective in predicting the onset of PAF, compared to using either analyses independently. Also, the Mixture of Experts classification yielded more precise class discrimination than other well-known classifiers. The performance of the proposed method was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which led to sensitivity, specificity, and accuracy of 100%, 95.55%, and 98.21% respectively. CONCLUSION: Prediction of PAF has been a matter of clinical and theoretical importance. We demonstrated that utilising an optimized combination of - as opposed to being restricted to - linear, time-frequency, and nonlinear features, along with applying the Mixture of Experts, contribute greatly to an early detection of PAF, thus, the proposed method is shown to be superior to those mentioned in similar studies in the literature.

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