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Living conditions of patients with refractory epilepsy will be significantly improved by a successful prediction of epileptic seizures. A proper warning impending seizure system should be resulted not only in high accuracy and low false positive alarms but also in suitable prediction time. In this study, the mean phase coherence index was used as a reliable indicator for identifying the pre-ictal period of 21-patient Freiburg dataset. In order to predict the seizures on-line, an adaptive Neuro-fuzzy model named ENFM (evolving Neuro-fuzzy model) was used to classify the extracted features. The ENFM was trained by a new class labeling method based on the temporal properties of a prediction characterized by two time intervals, seizure prediction horizon (SPH) and seizure occurrence period (SOP), which are subsequently applied in evaluation method. It is evident that increasing the SPH duration can be more beneficial to patients in preventing irreparable consequences of the seizure, as well as providing adequate time to deal with the seizure. In addition, a reduction in SOP duration can reduce the patient’s stress in SOP interval. These two theories motivated us to design Mamdani fuzzy inference system considering sensitivity and FPR of the prediction result in order to find optimal SOP and SPH for each patient. 10-patient dataset assigned for optimizing the fuzzy system, while the rest of data was used to test the model. The results showed that mean SOP by 6 min and mean SPH by 27 min provided the best outcome, so that last seizure as well as about 15-h inter-ictal period of each patient were predicted on-line without false negative alarms, yielding on average 100% sensitivity, 0.13 per hour FPR, 86.95% precision and 92.5% accuracy.

In this research, the mean phase coherence index used as a reliable indicator for identifying the preictal period of the 14-patient Freiburg EEG dataset. In order to predict the seizures on-line, an adaptive Neuro-fuzzy model named ENFM (evolving neuro-fuzzy model) was used to classify the extracted features. The ENFM trained by a new class labeling method based on the temporal properties of a prediction characterized by two time intervals, seizure prediction horizon (SPH), and seizure occurrence period (SOP), which subsequently applied in the evaluation method. It is evident that an increase in the duration of the SPH can be more useful for the subject in preventing the irreparable consequences of the seizure, and provides adequate time to deal with the seizure. Also, a reduction in duration of the SOP can reduce the patient’s stress in the SOP interval. In this study, the optimal SOP and SPH obtained for each patient using Mamdani fuzzy inference system considering sensitivity, false-positive rate (FPR), and the two mentioned points, which generally ignored in most studies.

Patients with anxious depression have more severe symptoms, more side effects, and higher resistance to treatment than patients with non-anxious depression; therefore, it is crucial to clarify the differences between these two types of patients. In this study, a 5-minute resting EEG was recorded in 15 patients with anxious depression and 9 patients with non-anxious depression under eyes open and closed conditions. Sixty-eight subcortical regions were extracted using exact low resolution brain electromagnetic tomography (eLORETA). The directed transfer function was then used to construct brain networks. Specific features based on graph theory including the strength of connectivity and betweenness centrality (BC) were calculated from the networks. Finally, significant features were selected using the Mann-Whitney U test, and patients were classified into anxious and non-anxious depressive groups using the Support Vector Machine (SVM). Results showed that features of outward connectivity strength led to the highest accuracy, F-score, and specificity with 91.66%, 87. 5%, and 100% in the eyes-closed state, respectively. Moreover, we found that the strength of connectivity in both directions increased for the anxious depressive group during the eyes-open state. In particular, higher outward connectivity was observed in the right hemisphere for the anxious depressive group. Further findings also revealed that features with the most significant difference were mainly associated with the beta band. In addition, significant increased inward and outward connectivity and decreased nodal centrality were observed in the posterior regions of the default mode network. These preliminary findings might provide new insights into the recognition of anxious depressed patients.

Psychogenic non-epileptic seizures (PNES) are often misdiagnosed as epileptic seizures (ES), leading to inappropriate treatment and delayed psychological care. To address this challenge, we analysed electroencephalogram (EEG) data from 74 patients (46 PNES, 28 ES) using one-minute preictal and interictal recordings per subject. Nine entropy measures (Sample, Fuzzy, Permutation, Dispersion, Conditional, Phase, Spectral, Rényi, and Wavelet entropy) were evaluated individually to classify PNES from ES using k-nearest neighbours, Naïve Bayes, linear discriminant analysis, logistic regression, support vector machine, random forest, multilayer perceptron, and XGBoost within a leave-one-subject-out cross-validation framework. In addition, a dynamic state, defined as the entropy difference between interictal and preictal periods, was examined. Sample, Fuzzy, Conditional, and Dispersion entropy were higher in PNES than in ES during interictal recordings (not significant), but significantly lower in the preictal (p < 0.05) and dynamic states (p < 0.01). Spatial mapping and permutation-based importance analyses highlighted O1, O2, T5, F7, and Pz as key discriminative channels. Classification performance peaked in the dynamic state, with Fuzzy entropy and support vector machine achieving the best results (balanced accuracy = 72.4%, F1 score = 77.8%, sensitivity = 74.5%, specificity = 70.4%). These results demonstrate the potential of entropy features for differentiating PNES from ES.

Objective: Differentiating functional/dissociative seizures (FDS) from epileptic seizures (ES) remains clinically challenging, with limited electrocardiogram (ECG) biomarker reliability. This study evaluated whether explainable machine learning applied to ECG features could identify autonomic markers for FDS–ES discrimination.

Methods: ECG recordings from 125 patients with FDS (n = 83) or ES (n = 42) from two epilepsy centres were analysed. A number of heart rate, HRV, and morphological ECG features was extracted from interictal and preictal segments. Relative-change features were calculated by normalising preictal values to interictal baseline. Classification used Leave-One-Subject-Out Cross-Validation with mutual information filtering, SHapley Additive exPlanations (SHAP)-guided feature selection, class balancing, and hyperparameter tuning.

Results: Entropy-based HRV measures were the most consistent discriminative features. In FDS, Sample Entropy, Fuzzy Entropy, and Dispersion Entropy decreased significantly from interictal to preictal states, whereas no significant entropy modulation was observed in ES. Dispersion Entropy showed the strongest contribution across statistical testing, SHAP interpretation, and feature-selection stability. Classification was limited in the interictal condition, and the best performance was obtained using relative-change features: XGBoost achieved 73.5% sensitivity (95% confidence interval [CI]: 62.7–82.6%) and 61.9% specificity (95% CI: 45.6–76.4%).

Conclusions: FDS was associated with preictal reduction in HRV entropy, indicating more regular, less complex cardiac dynamics. Within-subject changes appeared to provide more discriminative than static ECG features. Although current performance does not support standalone diagnostic use, entropy-based HRV measures offer interpretable peri-ictal autonomic markers, suggesting visceral changes contribute to FDS emergence.