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Initially, the proposed single-layer and two-layer LSTM models have been evaluated for EEG segments having durations in the range of 5\u201350 s for 24 epileptic subjects, out of which EEG segments of 30 s duration are found to be useful for accurate seizure prediction using two-layer LSTM model. Afterwards, to validate the performance of this classifier, the spectral features of 30 s duration EEG segments are fed to random forest, decision tree, k-nearest neighbour, support vector machine, and naive Bayes classifiers, which are empowered with grid search-based parameter estimation. Finally, the iterative simulation results and comparison with recently published existing techniques firmly reveal that the proposed two-layer LSTM model with EEG spectral features is an effective technique for accurately predicting seizures in real time with an average classification accuracy of 98.14%, average sensitivity of 98.51%, and average specificity of 97.78%, thereby enabling the epileptic patients to have a better quality of life.<\/jats:p>","DOI":"10.1007\/s40747-021-00627-z","type":"journal-article","created":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T07:03:11Z","timestamp":1643871791000},"page":"2405-2418","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":99,"title":["Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1465-6740","authenticated-orcid":false,"given":"Kuldeep","family":"Singh","sequence":"first","affiliation":[]},{"given":"Jyoteesh","family":"Malhotra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,3]]},"reference":[{"key":"627_CR1","unstructured":"NINDS (2021) Focus on Epilepsy Resarch: National Institute of Neurological Disorders and Stroke. https:\/\/www.ninds.nih.gov\/Current-Research\/Focus-Research\/Focus-Epilepsy. 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