AI-Based Epileptic Seizure Detection and Prediction in Internet of Healthcare Things: A Systematic Review

Abstract

Epilepsy is a neurological condition affecting around 50 million individuals worldwide, reported by the World Health Organization. This is identified as a hypersensitive disease by clinical associations. The unique characteristics of Electroencephalography have proven to be stable and universal; therefore, researchers have a lot of credibilities. So, it is the most used test for Epileptic Seizure detection and prediction. This study examines the contributions that have so far been made utilizing Electroencephalography technology to detect, predict, and monitor Epileptic Seizures. We have reviewed around 56 research articles, and those papers are selected from different academic databases. The studies explored various approaches, including Machine Learning, Deep Learning, and the Internet of Things framework. A comprehensive discussion of different classification algorithms is analyzed, and their performances are explored. Furthermore, various open issues of the stated approach are discussed, and potential future works are addressed.

Keywords

• Electroencephalography
• brain-computer interface
• artificial intelligence
• Internet of Things
• deep learning