A systematic review of 56 research articles evaluated the performance of Machine Learning, Deep Learning, and Internet of Things frameworks for EEG-based epileptic seizure detection and prediction.
Systematic Review (n=56)
This systematic review summarizes the current state of AI and IoT frameworks for EEG-based epileptic seizure detection and prediction.
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.
Jahan et al. (Sun,) conducted a systematic review in Epilepsy (n=56). AI-Based Epileptic Seizure Detection and Prediction using EEG was evaluated on Performance of different classification algorithms. A systematic review of 56 research articles evaluated the performance of Machine Learning, Deep Learning, and Internet of Things frameworks for EEG-based epileptic seizure detection and prediction.