Abstract
Epileptic seizure appears owing to the disorder in brain functioning that damages the health of the patients. Identification of seizures in the earlier stage is helpful to prevent seizure from treatment. Machine learning (ML) and computational models are applied to predict the seizures from electroencephalograms (EEG) signals. EEG signal-based epileptic seizure identification is a hot research topic that proficiently identifies the non-stationary development of brain activities. Basically, epilepsy is identified by physicians based on the visual reflection of EEG signals which is a tedious and time-consuming task. This article presents an efficient epileptic seizure detection and classification using the ML-based artificial neural network (ANN) model. The ANN is a biologically evolved computational model that activates a system for learning tracking details. It is utilized commonly for developing the prediction results. The performance of the proposed ANN model undergoes validation using EEG signal dataset, and the experimentation outcome verified the superior performance of the ANN model.
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Suguna, P., Kirubagari, B., Umamaheswari, R. (2021). An Analysis of Epileptic Seizure Detection and Classification Using Machine Learning-Based Artificial Neural Network. In: Suma, V., Chen, J.IZ., Baig, Z., Wang, H. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1395-1_5
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DOI: https://doi.org/10.1007/978-981-16-1395-1_5
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