Prediction and Recognition of Epileptic Seizures Based on Artificial Intelligence

Abstract

Repetitive seizures caused by abnormal brain activity are the hallmark of the common neurological illness epilepsy, which has a serious impact on the health of those affected. The introduction of artificial intelligence (AI) in recent years has ushered in novel ways of predicting and recognizing epileptic seizures, transforming the discipline of neurology. In particular, Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Support Vector Machines (SVMs), and Extreme Learning Machines (ELMs) are examined in this research as potential AI approaches for the prediction and identification of epileptic episodes. The underlying working logic of these AI approaches is investigated, emphasizing their distinct strengths and capacities. RNNs and LSTMs are well-known for their ability to describe temporal dependencies in sequential data, whereas SVMs excel in robust classification and ELMs excel at rapid training and real-time prediction. Real-world case studies show how these AI models may be used to analyze electroencephalogram (EEG) data, extract relevant features, and distinguish between seizure and non-seizure states. Furthermore, the essay explores the ramifications of AI in the medical profession, emphasizing the possibility of prompt intervention, improved patient care, and resource optimization. The difficulties and ethical concerns regarding AI in healthcare are also discussed. Finally, this piece highlights AI's promising role in predicting and identifying epileptic seizures, providing a glimpse into the future of personalized, data-driven healthcare. The combination of AI approaches opens the door to more accurate and timely seizure forecasts, increasing the quality of life for those living with epilepsy and revolutionizing the landscape of neurological healthcare.