A Comprehensive Analysis of Automatic Epilepsy Detection Techniques: Problems and Limitations

Recurrent seizures are a hallmark of the neurological illness epilepsy, which presents formidable diagnostic and therapeutic obstacles. There is significant potential for newly created automated seizure detection systems that use Machine Learning (ML) and Deep Learning (DL) approaches to improve patient care and expedite intervention. Electroencephalograms (EEGs) are a mainstay in the diagnosis of epilepsy, and Recurrent Neural Networks (RNNs) have lately gained prominence due to their ability to mimic temporal correlations in sequential data. Recent advances in automated epilepsy diagnosis using ML and DL are thoroughly covered in this work. The current research goes over the use of RNNs in seizure detection using EEG data and the several RNN designs that may be used for this task, including Long Short-Term Memory (LSTM), Bi-Directional Long short-term memory (BiLSTM), and Gated Recurrent Unit (GRU). This research takes a look at several problems and limitations associated with ML and DL-based approaches, including data scarcity, inter-subject variability, and the interpretability of models. Additionally, the current research emphasizes potential avenues for further study that might strengthen and improve ML and DL-based systems for epilepsy diagnosis, such as eXplainable Artificial Intelligence (XAI) methods, transfer learning, and multi-modal data fusion. Together, the study’s results and suggestions illuminate two very important areas: automated epilepsy diagnosis based on ML and DL and their current and future state of development.