A review of epileptic seizure detection using machine learning classifiers.

Area of Science:

• Neurology
• Biomedical Engineering
• Data Science

Background:

• Epilepsy is a chronic neurological disorder characterized by recurrent seizures.
• Brain signals, monitored via Electroencephalogram (EEG) and Electrocorticography (ECoG), are complex, noisy, and high-volume.
• Accurate seizure detection and understanding brain activity are challenging due to signal complexity.

Purpose of the Study:

• To provide an overview of machine learning techniques for epilepsy seizure detection.
• To categorize methods based on statistical features and machine learning classifiers ('black-box' and 'non-black-box').
• To highlight current research and future directions in seizure detection and classification.

Main Methods:

• Review of recent literature on seizure detection using machine learning.
• Analysis of various statistical features extracted from EEG/ECoG signals.
• Classification of machine learning approaches into 'black-box' and 'non-black-box' models.

Main Results:

• Machine learning classifiers demonstrate efficacy in classifying EEG data and detecting seizures.
• Selection of appropriate classifiers and features is crucial for optimal performance.
• Various approaches exist, utilizing diverse statistical features and machine learning algorithms.

Conclusions:

• Machine learning offers a promising avenue for accurate epilepsy seizure detection and classification.
• Further research is needed to optimize feature selection and classifier choice.
• This overview provides a foundation for understanding state-of-the-art seizure detection methods.