Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 270-277· 0 citations· 18 references
Abstract
Neurological illness detection using electroencephalograms (EEGs) is not an easy task due to the nature of brain signals, which are not stationary and noisy. In order to obtain the correct multi-class classification, the paper develops a hybrid deep learning framework that includes pre-processing, graph-based feature generation and efficient time modelling. The initial step of cleaning the data is to eliminate any missing data, or artefacts in EEG signals to obtain higher resulting output. A Graph Convoluted Network (GCN) is then used to approximate the functional connectivity between the EEG electrodes and each of the channels is represented as a node in a modelled graph. The GCN models spatial relationships and gets trained on features that are of relevance to the neurological patterns. The Bidirectional Long Short-Term Memory (BiLSTM) network receives features to acquire forward and backward statistical relationships. To enhance the convergence and speed of the weight selection process, the Enhanced Marine Predators Algorithm (EMPA) is applied to avoid falling into the local minima. Proposed model is more effective with higher accuracy of precision ,recall value and F1-score value. The experimental results demonstrating a sufficient recall of spatio-temporal behaviour and classification performance make the proposed model useful to be used in clinical decision support systems.
Epilepsy is a common chronic neurological disease. As an important tool for epilepsy diagnosis and disease assessment, electroencephalogram (EEG) can reflect the abnormal discharge activity of brain neurons. However, traditional EEG interpretation relies heavily on expert manual analysis, which has problems such as time-consuming, strong subjectivity and low efficiency. To improve the automation level of seizure detection, this paper proposes a pyramid-type one-dimensional convolutional neural network model (SE-P1D-LSTM) that integrates a channel attention mechanism and a Bidirectional Long Short-Term Memory (BiLSTM) network. The method first preprocesses the EEG signal through overlapping sliding window and Z-Score standardization, and combines Gaussian noise disturbance and random amplitude scaling for data augmentation; Then use the pyramidal one-dimensional convolutional structure to extract local temporal features, and adaptively strengthen the key channel information through SEBlock; Finally, a BiLSTM is introduced to model the long-range temporal dependencies in EEG signals to realize the automatic classification of normal and seizure EEG signals. The experiment was carried out based on the public epilepsy EEG dataset of the University of Bonn in Germany, and the performance of the model was evaluated using 10-fold cross-verification. The results show that the method in this paper has achieved an accuracy rate of 99.87%, a sensitivity of 99.87% and a specificity of 99.87% in the two-classification task, which is better than a variety of comparative models. The research results show that SE-P1D-LSTM can effectively extract the discriminative characteristics of epilepsy EEG signals, and has good classification performance and application potential in the automatic detection task of seizures.
Xien Gao· Computers and artificial int...· 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
A Deep Hybrid Neural Network framework that combines Convolutional Neural Networks (CNN) with the Aquila Optimizer (AO) for the automatic detection of epileptic seizures utilizing EEG data in MATLAB is introduced.
Swati Chowdhuri, Tiyasha Mondal· International Journal of Eng...· 0 citations
Accurate interpretation of electroencephalography (EEG) remains a major challenge in epilepsy diagnosis, particularly given that patient heterogeneity hinders the cross-subject generalization of artificial intelligence based algorithms. To address the complex spatio-temporal characteristics and long-range contextual dependencies of EEG signals, we propose a novel EEG decoding frame work. The framework employs a graph convolutional neural network to capture the spatial dependencies across EEG channels, integrated with a multiresolution transformer block to capture short-term dynamics while preserving long-term contextual information. This framework is used to develop a novel seizure detection algorithm, which has been validated on a large multicenter dataset in a cross subject manner. The algorithm demonstrated superior performance across diverse patient populations, achieving an accuracy of 96.5% and a false alarm rate of 1.61/h. To facilitate downstream clinical and research applications, we establish EEG-X, a cloud-based collaborative frame work that integrates the seizure detection algorithm along with our previously established interictal epileptiform detection and EEG source imaging methods. The framework supports collaborative annotation, enables browser-based multi-platform access without requiring specific software installation, and facilitates continuous monitoring in both clinical and home settings, demonstrating the feasibility and potential efficiency gains toward the practical implementation of quantitative EEG analysis for clinicians and researchers.
Pei Feng Tong, Long Zhang, Bosi Dong et al.· IEEE journal of biomedical a...· 0 citations
Drowsy driving EEG recognition faces challenges such as signal non-stationarity, imbalanced class distribution, and insufficient utilization of spatio-temporal features. To improve fatigue state recognition performance, this paper proposes an EEG-based drowsy driving recognition method that integrates multi-scale temporal features and brain functional connectivity. The proposed method takes 32-channel EEG signals from the public MPD-DF driving fatigue dataset as the research object. The raw EEG signals are first preprocessed, and a random oversampling strategy is applied only to the training set to alleviate class imbalance. In terms of model construction, a dual-branch network architecture is designed. On the one hand, a multi-scale EEGNet is employed to extract short-term, medium-term, and long-term EEG temporal features through convolutional kernels of different scales. On the other hand, brain functional connectivity matrices are constructed based on the weighted phase lag index (WPLI), and a graph convolutional network (GCN) is used to extract spatial topological features among EEG channels. Finally, the two types of features are fused and fed into fully connected layers to classify awake and fatigued states. Experimental results show that the proposed model outperforms baseline methods such as SVM, LSTM, and EEGNet, demonstrating its effectiveness in drowsy driving recognition.
Yongjing Yan, Guanghui Yan, Jun Zhang· 2026 8th International Confe...· 0 citations
Electrical status epilepticus during sleep (ESES) is a severe but often underdiagnosed epileptic encephalopathy that can significantly impair cognitive development in children. Current diagnostic practices rely heavily on expert visual inspection of spike-and-slow-wave complexes in electroencephalogram (EEG) recordings. This process is time-consuming, labor-intensive, and subjective, often resulting in poor inter-rater agreement. To address these limitations, this paper introduces a novel ESES automatic detection framework based on a large pre-trained EEG model. The framework employs a pre-trained NeuroEncoder to extract temporal and frequency features from spike-and-slow-wave complexes, and uniquely incorporates a temporal and spatial embedding module to enhance the model's ability to recognize spatiotemporal patterns. By leveraging the multi-head attention mechanism of stacked Transformers, the framework further captures complex relationships and deep features. A symmetric label smoothing cross-entropy is introduced, which applies both forward and backward constraints to reduce overfitting to noisy labels and improve robustness to label noise and individual variability. The proposed framework can automatically quantify spike-and-slow-wave complexes and calculate the spike-wave index (SWI) for ESES diagnosis. Evaluation was conducted on EEG recordings from 35 patients using a leave-one-subject-out cross-subject strategy. The results demonstrate excellent performance, with an accuracy of 91.91%, sensitivity of 91.52%, and specificity of 92.37%. The high agreement between the model and expert annotation is confirmed by a Kappa coefficient of 0.8368 and Gwet's AC1 of 0.8394. The proposed method demonstrates superior generalization across subjects and outperforms existing approaches. The proposed framework provides an efficient and reliable tool for automatic ESES detection, supporting early intervention and improved clinical management for patients with ESES.
Yuxuan Li, Zhipeng He, Shishi Tang et al.· International Journal of Neu...· 0 citations
