{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T23:54:02Z","timestamp":1775346842953,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Prince Sattam bin Abdulaziz University","award":["PSAU\/2024\/01\/29858"],"award-info":[{"award-number":["PSAU\/2024\/01\/29858"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The brain acts as the body\u2019s central command, overseeing diverse functions including thought, memory, speech, movement, and the regulation of various organs. When healthy, the brain functions seamlessly and automatically; however, disruptions can lead to serious conditions such as Alzheimer\u2019s Disease, Brain Cancer, Stroke, and Epilepsy. Epilepsy, a neurological disorder marked by recurrent seizures, results from irregular electrical activity in the brain. These seizures, which can strain both patients and neurologists, are characterized by symptoms like the loss of awareness, unusual behavior, and confusion. This study presents an efficient EEG-based epileptic seizure detection framework utilizing a hybrid Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models approach to support automated and accurate diagnosis. Handling imbalanced EEG data, which can otherwise bias model outcomes and reduce predictive accuracy, is a key focus. Experimental results indicate that the proposed framework generally outperforms other Deep Learning and Machine Learning techniques with the highest accuracy at 99.13%. Likewise, an Explainable Artificial Intelligence (XAI) called SHAP (SHapley Additive exPlanations) is utilized to analyze the results and to improve the interpretability of the models from medical decision-making. This framework aligns with the objectives of the Medical Internet of Things (MIoT), advancing smart medical applications and services for effective epileptic seizure detection.<\/jats:p>","DOI":"10.3390\/a18020077","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T12:18:56Z","timestamp":1738585136000},"page":"77","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Seizure Detection in Medical IoT: Hybrid CNN-LSTM-GRU Model with Data Balancing and XAI Integration"],"prefix":"10.3390","volume":"18","author":[{"given":"Hanaa","family":"Torkey","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia"},{"name":"Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt"}]},{"given":"Sonia","family":"Hashish","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt"},{"name":"Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia"}]},{"given":"Samia","family":"Souissi","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia"}]},{"given":"Ezz El-Din","family":"Hemdan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt"},{"name":"Structure and Materials Research Lab, Prince Sultan University, Riyadh 12435, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3114-2840","authenticated-orcid":false,"given":"Amged","family":"Sayed","sequence":"additional","affiliation":[{"name":"Department of Electrical Energy Engineering, College of Engineering & Technology, Arab Academy for Science Technology & Maritime Transport, Smart Village Campus, Giza 12577, Egypt"},{"name":"Industrial Electronics and Control Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"28827","DOI":"10.1007\/s11042-023-14504-z","article-title":"An efficient IoT based smart water quality monitoring system","volume":"82","author":"Hemdan","year":"2023","journal-title":"Multimed. 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