Aug 2026· 2026 IEEE International Conference on Mechatronics and Automation (ICMA)· pp. 252-257· 0 citations· 20 references
Abstract
Epilepsy and migraine are prone to clinical misdiagnosis due to overlapping clinical manifestations, while the limited availability of electroencephalogram (EEG) data further complicates accurate differential diagnosis. To address this challenge, this study proposes a three-class classification framework for epilepsy, migraine, and healthy controls based on EEG signals collected from 36 participants. After preprocessing the raw EEG data with a 0.5–60 Hz bandpass filter, 15 features were extracted, including 12 statistical features and three nonlinear dynamical features. Four machine learning models, namely support vector machine (SVM), random forest (RF), LightGBM, and XGBoost, were systematically evaluated for classification performance. Among them, XGBoost achieved the best overall results, with a test accuracy of 0.90 and superior performance across all major evaluation metrics. Feature importance analysis based on the gain metric of XGBoost identified the Hurst exponent as the most influential feature. Notably, its inter-group distribution differences were highly consistent with the pathological characteristics of epilepsy and migraine. These findings suggest that EEG-based feature fusion combined with machine learning provides an effective strategy for multi-class neurological disease classification, and that the Hurst exponent may serve as a promising biomarker for the differential diagnosis of epilepsy and migraine.
A clinically interpretable aEEG-CSA algorithm is feasible for neonatal seizure detection by extracting standard EEG features and coupling these features with a supervised ML classifier.
S. Edoigiawerie, J. Henry, B. Beaulieu-Jones et al.· medRxiv· 0 citations
Migraine is a complex neurological disorder characterised by recurrent headaches, sensory disturbances, and autonomic dysfunction. Despite its high prevalence, accurate diagnosis and effective management remain challenging due to the variability of symptoms and the absence of reliable biomarkers. In recent years, approaches based on artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have offered new perspectives for improving diagnostic accuracy and disease classification. ML models such as support vector machines and random forests have shown potential in identifying neuroimaging and electrophysiological biomarkers, while convolutional neural networks (CNNs) are capable of extracting meaningful patterns from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. Nevertheless, most studies are limited by small, homogeneous datasets and the lack of external validation, which restricts reproducibility and generalisability. Beyond technical performance, the transition of AI tools from research to clinical settings remains constrained by regulatory issues, limited interpretability, and difficulties in integration with existing healthcare systems. Future studies should focus on developing explainable and multimodal frameworks, supported by cross-institutional collaborations and prospective validation, to ensure both methodological robustness and clinical credibility. In the long term, the integration of such approaches may support earlier and more precise identification of migraine subtypes, guide individualised treatment strategies, and contribute to better patient outcomes.
A compact composite feature termed the Seizure Intensity Index (SII) together with an extended representation incorporating additional theta and alpha band descriptors is proposed together with an extended representation incorporating additional theta and alpha band descriptors for cross-patient seizure detection.
Schizophrenia is a chronic psychiatric disorder where the perception, cognition and behaviors are disturbed, which requires a proper and timely diagnosis to give an effective clinical intervention. EEG has been a recently viable non invasive technique for identifying neurophysiological markers related to schizophrenia. EEG-derived features, however, are usually very high dimensional and include redundant features and reduce classification performance. We propose a novel explainable machine learning approach for automated schizophrenia detection using EEG features. A publicly available EEG Psychiatric Disorders dataset was used to extract 1,144 features from both the demographic information and the EEG. To minimize feature redundancy, Analysis of Variance (ANOVA) based SelectKBest feature-selection was used to select 50 most discriminative-features. The hyperparameters of the Extra Trees classifier were then optimized by grid search and the selected features were classified using this optimized Extra Trees classifier. The model performance was assessed by an independent hold-out test set and Stratified-5-Fold Cross-Validation. The optimized model attained 95.35%, 95.29%, 95.83%, and 96.27% for hold-out test accuracy, balanced accuracy, F1-score and ROC-AUC, respectively. Moreover, the Stratified-5-Fold Cross-Validation provided a mean accuracy of 85.37 ± 5.01% and a mean ROC-AUC of 93.14 ± 2.51%, showing good generalization across the different data folds. Additionally, SHapley Additive exPlanations (SHAP) analysis revealed the most important demographic and EEG biomarkers for classification, improving the model interpretability. The results show that integrating ANOVA-based feature-selection with an optimized Extra Trees classifier, significantly improves classification performance while providing clinically interpretable predictions.
R. N. Panda, Anubhav Kumar· Genetics and Molecular Resea...· 0 citations
Epilepsy is neurological disorder which is a result of abnormal brain activity which causes repetitive seizures. The analysis of electroencephalogram (EEG) is vital in determining the pattern of epileptic and facilitating clinical diagnosis. Manual interpretation of EEG signals is very cumbersome and time consuming however, because of the high-dimensional and time varying nature of the brain signals. An automated epilepsy detection (EEG) framework is trained based on deep learning. It is a signal preprocessing, time frequency transformation, hierarchical feature extraction based on a hybrid neural architecture that encodes spatial and temporal EEG features. This is followed by the classification of the learned representations with the aim of obtaining the epileptic and non-epileptic brain activity patterns. The experimental assessment shows a better performance than the traditional procedures. The currently proposed model is significantly more accurate (96.7%), sensitive (96.0%), and specific (96.2%), as compared to classical machine learning models like support vector machines (91.2% accuracy) and random forest models (92.6% accuracy). Such results suggest that the improvement in performance of these techniques will be about 4-5 percent compared to the use of conventional techniques. The framework has a high potential of aiding in stable and automatic diagnosis of epilepsy in clinical settings.
Ritu Nagila, Kalaiyarasan R., M. S. et al.· 2026 International Conferenc...· 0 citations
This paper presents a comparative study for epilepsy monitoring using EEG signals along two main axes. The first axis consists of comparing the performance of the differentiation technique, which is known to be very important for the study of non-stationarity, with wavelet transform, which is widely used for detecting different brain rhythms. The second part aims to compare the performance of different machine learning algorithms, including k-Nearest Neighbors (k-NN), Decision Trees, Random Forest, and Support Vector Machines (SVM). Used features are statistical and higher order statistics characteristics such as mean, standard deviation, median, Min-Max, Kurtosis and Skewness. We tested our approach on publicly available and widely used datasets in the literature, namely the University of Bonn dataset and the Bern-Barcelona dataset. The experimental results demonstrate the effectiveness of the differentiation method as an important tool for EEG preprocessing, leading to very high performance.
Ines Bouzouita, Zayneb Brari, S. Belghith· International Conference on...· 0 citations