{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T14:41:11Z","timestamp":1792334471716,"version":"4.4.0"},"reference-count":48,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cutting-edge deep learning algorithms are applied to epileptic seizures to uncover hidden patterns and achieve high accuracy. Post hoc algorithms explain black-box models but do not reveal the detailed workings of the algorithms. Therefore, intrinsic methods overcome these issues; however, they suffer from improper use of indicative metrics and performance-to-accuracy trade-offs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study implements a lightweight multimodal explainable Neural Network for real-time early warning of epileptic seizure onset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>People with drug-resistant epilepsy across all age groups who had regular follow-up at Emanuel Mental Specialized Hospital in Ethiopia during the data collection period were the study population. EEG recordings were performed using the 10\/20 system. A pretest was conducted two weeks before actual data collection. EDFbrowser was used for preparing the dataset in CSV format. Learned and identity self-explainable neural network algorithms were used to develop a model. In addition to classical evaluation metrics, explicitness, faithfulness, stability, and clear patterns behind predictions were used. Ethical approval was also obtained from the institutional review board (IRB) with reference number No: AM\/146\/2\/36.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Learned and identity self-explainable neural networks were developed and compared. The models\u2019 accuracies are 96.524% and 95.328%, and their stability mean values are 99.88% and 99.88%, respectively. Additionally, explicitness values of 0.0055 and 0.01197 were achieved, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The results demonstrate that using a self-explainable neural network is a promising step toward responsible and transparent artificial intelligence. Comparing the two models shows that the learned self-explainable neural network is slightly more optimistic than the identity self-explainable neural network.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/09287329261498672","type":"journal-article","created":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T11:21:44Z","timestamp":1792149704000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Epileptic seizure onset early warning using a lightweight multimodal self-explainable neural network"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1526-7547","authenticated-orcid":false,"given":"Daraje Kaba","family":"Gurmessa","sequence":"first","affiliation":[{"name":"Department of Information Science, Faculty of Computing and Informatics, Institute of Technology, Jimma University, Jimma, Ethiopia"},{"name":"Department of Computer Science, Faculty of Engineering and Technology, Mattu University, Mattu, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kula Kekeba","family":"Tune","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7330-4054","authenticated-orcid":false,"given":"Worku","family":"Jimma","sequence":"additional","affiliation":[{"name":"Department of Information Science, Faculty of Computing and Informatics, Institute of Technology, Jimma University, Jimma, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,16]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"WHO. 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