Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data.

Autor:	Khalid M; School of Computer Science and Engineering, Central South University, Changsha, Hunan, China., Raza A; Department of Software Engineering, University Of Lahore, Lahore, Pakistan., Akhtar A; Institute of Business Administration, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan., Rustam F; School of Computer Science, University College Dublin, Dublin, Ireland., Ballester JB; Universidad Europea del Atlantico, Santander, Spain.; Universidad Internacional Iberoamericana Arecibo, Puerto Rico, USA.; Universidad de La Romana, La Romana, Republica Dominicana., Rodriguez CL; Universidad Europea del Atlantico, Santander, Spain.; Universidad Internacional Iberoamericana, Campeche, Mexico.; Universidade Internacional do Cuanza, Cuito, Angola., Díez IT; Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belen Valladolid, Spain., Ashraf I; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan South Korea.
Jazyk:	angličtina
Zdroj:	Digital health [Digit Health] 2024 Nov 05; Vol. 10, pp. 20552076241277185. Date of Electronic Publication: 2024 Nov 05 (Print Publication: 2024).
DOI:	10.1177/20552076241277185
Abstrakt:	Objective: Epileptic seizures are neurological events that pose significant risks of physical injuries characterized by sudden, abnormal bursts of electrical activity in the brain, often leading to loss of consciousness and uncontrolled movements. Early seizure detection is essential for timely treatments and better patient outcomes. To address this critical issue, there is a need for an advanced artificial intelligence approach for the early detection of epileptic seizure disorder. Methods: This study primarily focuses on designing a novel ensemble approach to perform early detection of epileptic seizure disease with high performance. A novel ensemble approach consisting of a fast, independent component analysis random forest (FIR) and prediction probability is proposed, which uses electroencephalography (EEG) data to investigate the efficacy of the proposed approach for early detection of epileptic seizures. The FIR model extracts independent components and class prediction probability features, creating a new feature set. The proposed model combined integrated component analysis (ICA) with predicting probability to enhance seizure recognition accuracy scores. Extensive experimental evaluations demonstrate that FIR assists machine learning models to obtain superior results compared to original features. Results: The research gap is addressed using combined features to improve the performance of epileptic seizure detection compared to a single feature set. In particular, the ensemble model FIR with support vector machine (FIR + SVM) outperforms other methods, achieving an accuracy of 98.4% for epileptic seizure detection. Conclusions: The proposed FIR has the potential for early diagnosis of epileptic seizures and can significantly help the medical industry with enhanced detection and timely interventions. Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. (© The Author(s) 2024.)
Databáze:	MEDLINE
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