{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T06:55:46Z","timestamp":1775544946631,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T00:00:00Z","timestamp":1672358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University, 388 Riyadh, Saudi Arabia","award":["PNURSP2023R178"],"award-info":[{"award-number":["PNURSP2023R178"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Visual analysis of an electroencephalogram (EEG) by medical professionals is highly time-consuming and the information is difficult to process. To overcome these limitations, several automated seizure detection strategies have been introduced by combining signal processing and machine learning. This paper proposes a hybrid optimization-controlled ensemble classifier comprising the AdaBoost classifier, random forest (RF) classifier, and the decision tree (DT) classifier for the automatic analysis of an EEG signal dataset to predict an epileptic seizure. The EEG signal is pre-processed initially to make it suitable for feature selection. The feature selection process receives the alpha, beta, delta, theta, and gamma wave data from the EEG, where the significant features, such as statistical features, wavelet features, and entropy-based features, are extracted by the proposed hybrid seek optimization algorithm. These extracted features are fed forward to the proposed ensemble classifier that produces the predicted output. By the combination of corvid and gregarious search agent characteristics, the proposed hybrid seek optimization technique has been developed, and is used to evaluate the fusion parameters of the ensemble classifier. The suggested technique\u2019s accuracy, sensitivity, and specificity are determined to be 96.6120%, 94.6736%, and 91.3684%, respectively, for the CHB-MIT database. This demonstrates the effectiveness of the suggested technique for early seizure prediction. The accuracy, sensitivity, and specificity of the proposed technique are 95.3090%, 93.1766%, and 90.0654%, respectively, for the Siena Scalp database, again demonstrating its efficacy in the early seizure prediction process.<\/jats:p>","DOI":"10.3390\/s23010423","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T03:08:59Z","timestamp":1672628939000},"page":"423","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals"],"prefix":"10.3390","volume":"23","author":[{"given":"Bhaskar","family":"Kapoor","sequence":"first","affiliation":[{"name":"Ambedkar Institute of Advanced Communication Technologies & Research (AIACT&R), Guru Gobind Singh Indraprastha University, New Delhi 110078, India"}]},{"given":"Bharti","family":"Nagpal","sequence":"additional","affiliation":[{"name":"NSUT (East Campus) (Formerly AIACT&R), Delhi 110031, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7651-4444","authenticated-orcid":false,"given":"Praphula Kumar","family":"Jain","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering & Applications, GLA University, Mathura 281406, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0169-6738","authenticated-orcid":false,"given":"Ajith","family":"Abraham","sequence":"additional","affiliation":[{"name":"Machine Intelligence Research Labs (MIR Labs), Auburn, WA 98071, USA"}]},{"given":"Lubna Abdelkareim","family":"Gabralla","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, College of Applied, Princess Nourah bint Abdulrahman University, Riyadh 11564, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,30]]},"reference":[{"key":"ref_1","unstructured":"(2021, August 12). 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