Jul 2026· 2026 International Conference on Advanced Computing and Knowledge Engineering (ICACKE)· pp. 1-6· 0 citations· 16 references
Abstract
A novel lightweight comparative ML-DL framework for detecting seizures with EEG signals recorded using the UCI Epileptic Seizure Recognition Dataset is presented in this article. The data collected from the experiments was adjusted to form a binary classification problem, where the elements of the dataset were divided into seizure versus non-seizure. The number of features was reduced from 178 to 148 with a variance feature reduction method while maintaining the discriminative quality of the features. Four traditional machine learning models were compared with a Long Short-Term Memory (LSTM) network model to capture the temporal aspects of the EEG signal. The Random Forest algorithm produced an overall accuracy of 97.08%, while the LSTM produced an accuracy of 98%. This finding is indicative that when a framework is developed with an LSTM and temporal modeling capabilities, the performance increases, therefore making it more suitable for real-time seizure detection.
A proposed method for detecting epileptic seizures from electroencephalogram data involves creating an optimal deep learning architecture that incorporates deep learning architectures, feature optimisation, and wavelet-based preprocessing.
M. Nanditha, A. S. Kumar, Saravanakumar Selvaraj et al.· Journal of Intelligent Decis...· 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
This study systematically introduces and evaluates 25 less-explored time-domain features, 13 of which have no documented precedent as classification features in scalp EEG seizure detection, against 25 classical features and their 50-feature combination.
Edgar H. Ayala-Britez, Lucas Frutos, D. Pinto-Roa et al.· Machine Learning and Knowled...· 0 citations
BACKGROUND
Early diagnosis of neurological dysfunctions, particularly epilepsy, is vital for early intervention and improvement of patients' quality of life. However, traditional seizure detection techniques suffer from low detection accuracy, high false positive rate, and high computational complexity, making it difficult to effectively capture the complex spatiotemporal characteristics of electroencephalography (EEG) signals. Despite the significant improvements in seizure detection accuracy brought by deep learning techniques, the current models suffer from inaccuracies, limited adaptability, and inability to operate in real time.
NEW METHOD
To address these challenges, a new hybrid deep learning model based on one-dimensional Convolutional Neural Network (1D-CNN), Bidirectional Long Short-Term Memory (BiLSTM) network and Dueling Q-Learning is proposed to accurately classify epileptic seizures from EEG signals in an adaptive manner. In addition, a novel approach is proposed called Metaheuristic Based Adaptive Optimization (MBAO) to adaptively select an optimal temporal window size for the effective extraction of features, while minimizing the required information loss and computation burden.
RESULTS
and Comparison with existing methods: The proposed model has tested in various experiments conducted in a large number of benchmark datasets like CHB-MIT, Kaggle EEG Epileptic datasets etc. which justifies the effectiveness of the proposed model. DuelQ-SeizureNet has an accuracy of 99%, a precision of 96%, a recall (sensitivity) of 98%, a specificity of 99%, and an F1 score of 99% with a low execution time of 50ms in seizure prediction.
CONCLUSIONS
This proposed framework introduces a novel reinforcement learning assisted optimization approach in deep seizure detection architecture. It can operate with lower false detection rates (1.8%), higher area under the ROC curve (AUC) (0.995), and lower computational speed than the existing scheme, ensuring reliable real-time implementation.
N. K, R. P· Journal of Neuroscience Meth...· 0 citations
The use of pre-trained models reduced the training time and resources required, and the unique application of the ensemble learning approach produces more robust and reliable results compared to individual deep learning models.
Unnati Chaurasia, Shilpa Sj, H. Pathak et al.· Discover Artificial Intellig...· 0 citations