Unveiling the epilepsy enigma: an agile and optimal machine learning approach for detecting inter-ictal state from electroencephalogram signals

Abstract Epilepsy is a chronic neurological disorder characterized by the occurrence of paroxysmal recurrent seizures, which are caused by abnormal electrical activity in the brain. Seizures vary widely in their presentation, depending on the specific region of the brain involved and the extent of the abnormal electrical discharges. The disease can affect cognitive function posing a serious threat to the patients’ lives. Epilepsy causes emotional and behavioral changes, along with sleep disorders and migraines, leading to social isolation and discrimination. Timely administration of medication can cure most cases of epilepsy. However, identifying epileptic patients requires reviewing multiple EEG signal sheets, which can delay disease prediction. Therefore, the aim of our study is to apply simplistic machine learning algorithms that can study the EEG signal data swiftly to identify individuals in seizure, inter-ictal, and normal states, thereby aiding in medical diagnosis. The novelty of this study lies in the utilization of pre-built methods and develop a fast and efficient model that is lightweight and easy to integrate in healthcare to provide relief to epileptic patients. While previous studies have achieved high accuracy, the discussion involving time complexity of their models has been scarce. Given the importance of timely medication in managing epilepsy, it is crucial to consider the runtime of the model rather than solely focusing on accuracy. Therefore, a model that balances both a short runtime (2.9 min) and a satisfactory accuracy (97.46%) has been developed in this project. Integration of this project's findings will catalyze transformative changes within the healthcare industry, enabling healthcare professionals to detect epilepsy at earlier stages and provide timely interventions, ultimately fostering a system that prioritizes precision, innovation, and improved patient outcomes.