International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 18, No. 5, 8 Oct. 2026
Cover page and Table of Contents: PDF (size: 2607KB)
Comprehensive Feature Fusion in Deep Learning Models for Robust Epileptic Seizure Detection from EEG Signals
PDF (2607KB), PP.97-121
Views: 0 Downloads: 0
Author(s)
Maneesh Kumar
1,*
Rakesh Kumar
1
Santosh Kumar
2
Index Terms
Epileptic Seizure Detection, Vision Transformer, Coordinate Attention-based Feature Fusion, Short-Time Fourier Transform, Adaptive Dilated Dense Recurrent Neural Network with Novel Activation Function, Modernized Exploration of Magnificent Frigatebird Optimiza
Abstract
Epilepsy is a prevalent neurological disorder characterized by recurrent epileptic seizures. After a stroke, it is a highly common neurological condition. For identifying seizures, the Electroencephalogram (EEG) is the gold-standard modality for capturing and recording the brain's electrical activity with high temporal resolution. The existing machine learning approaches have been leveraged before the emergence of deep learning. However, these models limited the performance as they included handcrafted features. The deep learning models perform the feature extraction automatically, which improves the classification accuracy compared with the conventional strategies. Therefore, in this article, an epileptic seizure detection approach on the basis of deep learning methods is developed to identify the abnormal brain functionalities in the initial stage. Initially, the EEG signal collection is carried out through standard benchmark databases. After that, the spectral and statistical features are directly extracted from the acquired EEG signals. Subsequently, the Short-Time Fourier Transform (STFT) is applied to transform the EEG signals into spectrograms, and then the features from those spectrograms are extracted via Vision Transformer (ViT). Furthermore, the Wave-based features are also extracted, which provide a comprehensive view of the brain activities. After that, a Coordinate Attention-based Feature Fusion mechanism is employed to fuse these diverse feature types, which captures complementary details from the EEG signals. The fused features are subjected to the Adaptive Dilated dense Recurrent neural Network with a Novel Activation Function (ADRNet-NAF) for the Epileptic seizure detection. The detection accuracy of the proposed model is improved by optimizing the hyperparameters of the ADRNet-NAF model using Modernized Exploration of Magnificent Frigatebird Optimization (MEMFO) during network training. The empirical analysis over the traditional methods is conducted to validate the Epileptic seizure detection performance of the proposed model using various measures. The proposed model achieves 94.98% accuracy, 89.30% sensitivity and 99.08% specificity.
Cite This Paper
Maneesh Kumar, Rakesh Kumar, Santosh Kumar, "Comprehensive Feature Fusion in Deep Learning Models for Robust Epileptic Seizure Detection from EEG Signals", International Journal of Information Technology and Computer Science(IJITCS), Vol.18, No.5, pp.97-121, 2026. DOI:10.5815/ijitcs.2026.05.07
Reference
[1]Cao, Xiaoshuai, Shaojie Zheng, Jincan Zhang, Wenna Chen, and Ganqin Du, "A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection," BMC Medical Informatics and Decision Making, vol. 25, no. 1, pp. 6, 2025.
[2]Xu, J., Yan, K., Deng, Z., Yang, Y., Liu, J.-X., Wang, J. and Yuan, S., "EEG-based epileptic seizure detection using deep learning techniques: A survey", Neurocomputing, vol. 610, pp. 128644, 2024, doi: 10.1016/j.neucom.2024.128644.
[3]Liu, Yingjian, Guoyang Liu, Shibin Wu, and Chung Tin, "Phase spectrogram of EEG from S-transform Enhances epileptic seizure detection," Expert Systems with Applications, vol. 262, 2025.
[4]Liu, Ying, Cai Xu, Ziqi Wen, and Yansong Dong, "Trust EEG epileptic seizure detection via evidential multi-view learning," Information Sciences, vol. 694, pp. 121699, 2025.
[5]Malekzadeh, A., Zare, A., Yaghoobi, M., Kobravi, H.R. and Alizadehsani, R., "Epileptic seizures detection in EEG signals using fusion handcrafted and deep learning features", Sensors, vol.21, no.7710, 2021.
[6]Mekruksavanich, Sakorn, Wikanda Phaphan, and Anuchit Jitpattanakul, "Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism," Mathematical Biosciences and Engineering, vol. 22, no. 1, pp. 73-105, 2025.
[7]Li, Jia Wen, Guan Yuan Feng, Ju Jian Lv, Rong Jun Chen, Lei Jun Wang, Xian Xian Zeng, Jun Yuan, Xiang Lei Hu, Hui Min Zhao, and Xu Lu, "A rhythmic encoding approach based on EEG time-frequency image for epileptic seizure detection," Biomedical Signal Processing and Control, vol. 99, pp. 106824, 2025.
[8]Xu, Yankun, Junzhe Wang, Yun-Hsuan Chen, Jie Yang, Wenjie Ming, Shuang Wang, and Mohamad Sawan, "VSViG: Real-Time Video-Based Seizure Detection via Skeleton-Based Spatiotemporal ViG," In European Conference on Computer Vision, pp. 228-245, Springer, Cham, 2025.
[9]Mallik, Puspanjali, Ajit Kumar Nayak, Sumanta Kumar Mohapatra, and K. P. Swain, "Effectiveness of SOSA, FFA and GOA Algorithms for Epileptic Seizure Detection Using DWT and SVM," In Computing, Communication and Intelligence, pp. 52-56. CRC Press, 2025.
[10]Hu, Dinghan, Kai Wu, Yuan Fang, Tiejia Jiang, Feng Gao, and Jiuwen Cao, "STMemAE: An Instance-Level Based Spatio-Temporal Memory Autoencoder for Unsupervised Vision-Based Seizure Detection," IEEE Transactions on Emerging Topics in Computational Intelligence, 2025.
[11]Niu, Ruili, Xuan Guo, Jiaoyang Wang, and Xiaofeng Yang, "The hidden rhythms of epilepsy: exploring biological clocks and epileptic seizure dynamics," Acta Epileptologica, vol. 7, no. 1, pp. 1, 2025.
[12]Dokare, Indu, and Sudha Gupta. "AI-Driven Seizure Detection using EEG: A Synergistic Approach with DWT, Entropy Features and ANN," In Technologies for Energy, Agriculture, and Healthcare, pp. 129-138, CRC Press, 2025.
[13]Aayesha, M. B. Qureshi, M. Afzaal, M. S. Qureshi and M. Fayaz, "Machine learning-based EEG signals classification model for epileptic seizure detection", Multimedia Tools and Applications, vol. 80, no. 12, pp. 17849-17877, 2021, doi: 10.1007/s11042-021-10597-6.
[14]Chandrasekar, Jamunadevi, and Arul Ponnusamy, "Hybrid jellyfish search sine cosine optimisation-based deep learning for big data classification using MapReduce framework on epileptic seizure data," International Journal of Intelligent Information and Database Systems, vol. 17, no. 1, pp. 1-31, 2025.
[15]Gramacki, A. and Gramacki, J., "A deep learning framework for epileptic seizure detection based on neonatal EEG signals", Scientific reports, vol.12, no.13010, 2022.
[16]Karasmanoglou, Apostolos, Giorgos Giannakakis, Pelagia Vorgia, Marios Antonakakis, and Michalis Zervakis, "Semi-Supervised anomaly detection for the prediction and detection of pediatric focal epileptic seizures on fused EEG and ECG data," Biomedical Signal Processing and Control, vol. 101, pp. 107083, 2025.
[17]Chen, Guibin, Gang Li, Wanxiu Xu, Hanfan Wu, Suhong Ye, and Bin Zhou, "A DF-SSA analytical framework for revealing variations in multidimensional EEG features of epileptic seizures," Biomedical Signal Processing and Control, vol. 100, pp. 107073, 2025.
[18]Wong, Sheng, Anj Simmons, Jessica Rivera-Villicana, Scott Barnett, Shobi Sivathamboo, Piero Perucca, Zongyuan Ge, Patrick Kwan, Levin Kuhlmann, and Terence J. O’Brien, "Channel-annotated deep learning for enhanced interpretability in EEG-based seizure detection," Biomedical Signal Processing and Control, vol. 103, pp. 107484, 2025.
[19]Aboyeji, Sunday Timothy, Ijaz Ahmad, Xin Wang, Yan Chen, Chen Yao, Guanglin Li, Michael Chi Fai Tong, Alice KY Siu, Guoru Zhao, and Shixiong Chen, "DCSENets: Interpretable deep learning for patient-independent seizure classification using enhanced EEG-based spectrogram visualization," Computers in Biology and Medicine, vol. 185, pp. 109558, 2025.
[20]Dişli, Fırat, Mehmet Gedikpınar, Hüseyin Fırat, Abdulkadir Şengür, Hanifi Güldemir, and Deepika Koundal, "Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model," Diagnostics, vol. 15, no. 1, pp. 84, 2025.
[21]Guendelman, Miriam, Rotem Vekslar, and Oren Shriki, "A New Perspective in Epilepsy Classification: Applying the Taxonomy of Seizure Dynamotypes to Non-Invasive EEG and examining dynamical changes across sleep stage," eNeuro, 2025.
[22]Ahmad, I., Wang, X., Javeed, D., Kumar, P., Samuel, O.W. and Chen, S., "A hybrid deep learning approach for epileptic seizure detection in EEG signals", IEEE Journal of Biomedical and Health Informatics, 2023.
[23]Hepseeba Kode, Khaled Elleithy, And Laiali Almazaydeh, "Epileptic Seizure Detection in EEG Signals Using Machine Learning and Deep Learning Techniques," IEEE Access, 2024.
[24]Manuel J. Rivera, Javier Sanchis, Oscar Corcho, Miguel A. Teruel, Andjuan Trujillo, "Evaluating CNN Methods for Epileptic Seizure Type Classification Using EEG Data, "IEEE Access, 2024.
[25]Akira Furui, Ryota Onishi, Tomoyuki Akiyama, And Toshio Tsuji, "Epileptic Seizure Detection Using a Recurrent Neural Network With Temporal Features Derived From a Scale Mixture EEG Model," IEEE Access, 2024.
[26]Kamini Kamakshi Palanisamy And Arthi Rengaraj, "Early Detection of Stress and Anxiety Based Seizures in Position Data Augmented EEG Signal Using Hybrid Deep Learning Algorithms," IEEE Access, 2024.
[27]Yayan Pan, Fangying Dong, Wei Yao, Xiaoqin Meng, And Yongan Xu, "Empirical Mode Decomposition for Deep Learning-Based Epileptic Seizure Detection in Few-Shot Scenario," IEEE Access, 2024.
[28]Tamilarasi Kathirvel Murugan, Anush Kameswaran, "Employing convolutional neural networks and explainable artificial intelligence for the detection of seizures from electroencephalogram signaL, " Results in Engineering, vol. 24, pp. 103378, 2024.
[29]Achraf Djemal, Ahmed Yahia Kallel, Cherif Ouni, Rihem El Baccouch, Dhouha Bouchaala, Fatma Kammoun Feki, Chahnez Charfi Triki, Ahmed Fakhfakh, Olfa Kanoun, "Fast processing and classification of epileptic seizures based on compressed EEG signals," Computers in Biology and Medicine, vol. 184, pp. 109346, 2025.
[30]M.V.V. Prasad Kantipudi, N. S. Pradeep Kumar, RajanikanthAluvalu, Shitharth Selvarajan & K Kotecha, "An improved GBSO‑TAENN‑based EEG signal classification model for epileptic seizure detection," Scientific Reports, vol. 14, pp. 843, 2024.
[31]Zubair, M., Belykh, M.V., Naik, M.U.K., Gouher, M.F.M., Vishwakarma, S., Ahamed, S.R. and Kongara, R., "Detection of epileptic seizures from EEG signals by combining dimensionality reduction algorithms with machine learning models", IEEE Sensors Journal, vol.21, pp.16861-16869, 2021.
[32]Abdelhameed, A. and Bayoumi, M., "A deep learning approach for automatic seizure detection in children with epilepsy", Frontiers in Computational Neuroscience, vol.15, no.650050, 2021.
[33]Gabeff, V., Teijeiro, T., Zapater, M., Cammoun, L., Rheims, S., Ryvlin, P. and Atienza, D., "Interpreting deep learning models for epileptic seizure detection on EEG signals", Artificial intelligence in medicine, vol.117, no.102084, 2021.
[34]Poorani, S. and Balasubramanie, P.J.I.J.O.S.A.E., "Deep learning based epileptic seizure detection with EEG data", International Journal of System Assurance Engineering and Management, pp.1-10, 2023.
[35]Hassan, F., Hussain, S.F. and Qaisar, S.M., "Epileptic seizure detection using a hybrid 1D CNN‐machine learning approach from EEG data", Journal of Healthcare Engineering, no.9579422, 2022.
[36]Yogarajan, G., Alsubaie, N., Rajasekaran, G., Revathi, T., Alqahtani, M.S., Abbas, M., Alshahrani, M.M. and Soufiene, B.O., "EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network", Scientific Reports, vol.13, no.17710, 2023.
[37]Abdulwahhab, A.H., Abdulaal, A.H., Al-Ghrairi, A.H.T., Mohammed, A.A. and Valizadeh, M., "Detection of epileptic seizure using EEG signals analysis based on deep learning techniques", Chaos, Solitons & Fractals, vol.181, no.114700, 2024.
[38]Kunekar, P., Gupta, M.K. and Gaur, P., "Detection of epileptic seizure in EEG signals using machine learning and deep learning techniques", Journal of Engineering and Applied Science, vol.71, no.21, 2024.
[39]Varlı, M. and Yılmaz, H., "Multiple classification of EEG signals and epileptic seizure diagnosis with combined deep learning", Journal of Computational Science, vol.67, no.101943, 2023.
[40]Mekruksavanich, S. and Jitpattanakul, A., "Effective detection of epileptic seizures through EEG signals using deep learning approaches", Machine Learning and Knowledge Extraction, vol.5, pp.1937-1952, 2023.
[41]Dissanayake, T., Fernando, T., Denman, S., Sridharan, S. and Fookes, C., "Deep learning for patient-independent epileptic seizure prediction using scalp EEG signals", IEEE Sensors Journal, vol.21, pp.9377-9388, 2021.
[42]Hamadneh, Tareq, Khalid Kaabneh, Ibraheem AbuFalahah, Gulnara Bektemyssova, Galymzhan Shaikemelev, Dauren Umutkulov, Sayan Omarov, Zeinab Monrazeri, Frank Werner, and Mohammad Dehghani, "Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems," Computers, Materials and Continua, vol. 80, no. 2, pp.2721-2741, 2024.
[43]Kim, J.Y. and Lee, S.H., "Accuracy enhancement method for speech emotion recognition from spectrogram using temporal frequency correlation and positional information learning through knowledge transfer", arXiv preprint, 2024.
[44]Chen, C.F.R., Fan, Q. and Panda, R., "Crossvit: Cross-attention multi-scale vision transformer for image classification", Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 357-366, 2021.
[45]Ding, C., Chen, Y., Li, R., Wen, D., Xie, X., Zhang, L., Wei, W. and Zhang, Y., "Integrating hybrid pyramid feature fusion and coordinate attention for effective small sample hyperspectral image classification", Remote Sensing, vol.14, no.2355, 2022.
[46]Yoo, Y.H., Han, K., Cho, S., Koh, K.C. and Kim, J.H., "Dense Recurrent Neural Network with Attention Gate", 2018.
[47]Mukherjee, A. and Mitra, P., "Predicting cyclone landfall using mutual information and dilated recurrent neural network", Environmental Data Science, vol.1, no.e18, 2022.
[48]Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M. and Chen, H., "Harris hawks optimization: Algorithm and applications", Future generation computer systems, vol.97, pp.849-872, 2019.
[49]Ginidi, A., Ghoneim, S.M., Elsayed, A., El-Sehiemy, R., Shaheen, A. and El-Fergany, A., "Gorilla troops optimizer for electrically based single and double-diode models of solar photovoltaic systems", Sustainability, vol.13, no.9459, 2021.
[50]Dutta, T., Bhattacharyya, S., Dey, S. and Platos, J., "Border collie optimization", IEEE Access, vol.8, pp.109177-109197, 2020.