Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review

By Khansa Rasheed, Adnan Qayyum, Junaid Qadir, Shobi Sivathamboo, Patrick Kwan, Levin Kuhlmann, T. O'Brien, Adeel Razi. IEEE Reviews in Biomedical Engineering, 2020

323 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Psychiatry and Mental health, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1c6e9ee09531014889238dbe55b1bc94a13f5c3d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Ala Al-Fuqaha
2. Adeel Razi

In Psychiatry and Mental health

1. Adeel Razi
2. Adnan Qayyum

In Signal Processing

1. Ala Al-Fuqaha
2. Sridha Sridharan
3. Junaid Qadir
4. Clinton Fookes

Citations in the corpus

• Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “…deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks are widely employed for healthcare applications, i.e., epilepsy seizure prediction [52], segmentation of brain tumor [53], Alzheimer detection [54], genomics [55], and medical prescriptions [56].”
• Deep Learning for Medical Anomaly Detection – A Survey (2020, background): “detection have briefly touched upon the medical anomaly detection domain, and despite numerous survey papers published on specific medical application domains [12], [13], [14], [15], [16], there is no systematic review of deep learning based medical anomaly detection techniques which would allow rea”