Abstract
With the pervasive generation of medical data, there is a need for the worldwide medical and health care sector to find appropriate computational intelligence techniques for various medical conditions such as epilepsy seizures (ES). ES is a brain disorder that affects people of all ages, is a chronic, non-communicable disease, and can occur for no apparent reason owing to a genetic defect at any time. The unpredictable nature of ES poses a significant threat to human life where we have a target variable with five labels of seizure, namely pre-seizure, seizure and seizure-free, are classified using real clinical data. In order to accurately classify seizure activity (e.g., the target label) without extensive feature engineering or selection, we employ a deep learning classifier as the study’s baseline classifier. Deep learning is a branch of artificial intelligence and currently the most successful computational intelligence technique for diagnosing ES in health informatics. This paper deals with a real-life application of epilepsy classification using computational techniques namely, Target-vs-One and Target-vs-All using deep learning approach. It is investigated that the baseline classifier on Target-vs-One strategy achieved the highest f1-score and accuracy about 0.9815 and 0.9818, respectively, as compared to the performance of baseline classifier on Target-vs-All strategy (e.g., achieved 0.94 of f1-score and 0.98 of accuracy).
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URL: https://www.ukbonn.de/epileptologie/arbeitsgruppen/ag-lehnertz-neurophysik/downloads/ Last Access on 17 Oct, 2023 19:45.
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Acknowledgements
This work was supported by the FCT – Fundação para a Ciência e a Tecnologia, I.P. [Project UIDB/05105/2020].
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Amin, A., Al-Obeidat, F., Algeelani, N.A., Shudaiber, A., Moreira, F. (2024). Target-vs-One and Target-vs-All Classification of Epilepsy Using Deep Learning Technique. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Poniszewska-Marańda, A. (eds) Good Practices and New Perspectives in Information Systems and Technologies. WorldCIST 2024. Lecture Notes in Networks and Systems, vol 986. Springer, Cham. https://doi.org/10.1007/978-3-031-60218-4_9
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