Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review
Khansa Rasheeda b, Adnan Qayyuma b, Junaid Qadira b, Shobi Sivathambooc d e f, Patrick Kwanc d e f, Levin Kuhlmanng h i, Terence John O'Brienf, Adeel Razij k l m
- aInformation Technology University (ITU)-Punjab, Lahore, Pakistan
- b[Information Technology University (ITU)-Punjab, Lahore, Pakistan]
- cDepartment of Neurology, Alfred Health, Melbourne, VIC, Australia
- dDepartment of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- eDepartments of Neurology and Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, VIC, Australia
- fDepartment of Neuroscience Central Clinical School Monash University Melbourne VIC Australia
- gDepartment of Medicine, St. Vincent's Hospital, The University of Melbourne, Parkville, VIC, Australia
- hFaculty of Information Technology, Monash University, Clayton, VIC, Australia
- iFaculty of Information Technology, Monash University , Clayton, VIC , Australia
- jDepartment of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
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Abstract
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML-based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions.
TL;DRA comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals to identify the gaps, challenges, and pitfalls in the current research and recommend future directions. (Semantic Scholar)