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Article Abstract
Introduction: The seizure episodes result from abnormal and excessive electrical discharges by a group of brain cells. EEG framework-based signal acquisition is the real-time module that records the electrical discharges produced by the brain cells. The electrical discharges are amplified and appear as a graph on electroencephalogram systems. Different neurological disorders are represented as different waves on EEG records.
Method: This paper involves the detection of Epilepsy which appears as rapid spiking on electroencephalogram signals, using feature extraction and machine learning techniques. Various models, such as the Support Vector Machine, K Nearest Neighbor, and random forest, have been trained, and accuracy has been analyzed to predict the seizure.
Result: An average accuracy of 95% has been claimed using the optimized model for epileptic seizure detection during training and validation. During the analysis of multiple models, the 97% accuracy is claimed after testing. Some statistical parameters are calculated to justify the optimized framework.
Discussion: The proposed approach represents a satisfactory contribution in precise detection for smart healthcare.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12149152 | PMC |
| http://dx.doi.org/10.3389/fncom.2025.1545425 | DOI Listing |
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