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UBC Theses and Dissertations

Scalp and intracranial EEG quantitative analysis : robust detection and prediction of epileptic seizures Hussein, Ramy


Epilepsy is a common neurological disorder that affects over 90 million people globally — 30-40% of whom do not respond to medication. Electroencephalogram (EEG) is the prime tool that has been widely used for the diagnosis and management of epilepsy. As the visual inspection of long-term EEG is tedious, expensive, and time-consuming, research in the EEG-based methods to automatically detect and predict epileptic seizures has been very active. This thesis studies how to leverage the temporal, spectral, and spatial information in the EEG data to accurately detect and also predict seizures. To automatically detect epileptic seizures, we first introduce a computationally-efficient method that detects a seizure within a very short time of its onset. It relies on a computationally-simple feature extraction method based on LASSO regression and extracts the prominent EEG seizure-associated features in a time-efficient manner, achieving high seizure detection accuracy with very short detection latency. Subsequently, we propose two novel methods for robust detection of epileptic seizures where the main question addressed is: Can we identify the seizure pattern(s) hidden in the contaminated EEG data? We first present a novel feature learning algorithm based on L1-penalized robust regression. This algorithm extracts the most distinguishable EEG spectral features pertinent to epileptic seizures. We then propose a deep learning method that achieves a better robust performance under real-life conditions. This method uses long-short-term memory recurrent networks to exploit the temporal dependencies in the EEG data and accurately recognize the seizure patterns. Both methods are proven to maintain robust performance in the presence of common signal contaminants and ambient noise. The thesis then addresses the seizure prediction problem using intracranial EEG (iEEG) data. A novel architecture of multi-scale convolutional neural networks is proposed to learn the discriminative pre-seizure iEEG features that could potentially help predict impending seizures. Experiments on clinical data show that this method achieves high seizure prediction sensitivity and maintains reliable performance against inter- and intra-patient variations.

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