QuPWM: Feature Extraction Method for MEG Epileptic Spike Detection

Illustration of the brain abnormal activities for different types of epileptic seizure


Epilepsy is a neurological disorder classified as the second most serious neurological disease known to humanity, after stroke. Localization of epileptogenic zone is an important step for epileptic patient treatment, which starts with epileptic spike detection. The common practice for spike detection of brain signals is via visual scanning of the recordings, which is a subjective and a very time-consuming task. Motivated by that, this paper focuses on using machine learning for automatic detection of epileptic spikes in magnetoencephalography (MEG) signals. First, we used the Position Weight Matrix (PWM) method combined with a uniform quantizer to generate useful features. Second, the extracted features are classified using a Support Vector Machine (SVM) for the purpose of epileptic spikes detection. The proposed technique shows great potential in improving the spike detection accuracy and reducing the feature vector size. Specifically, the proposed technique achieved average accuracy up to 98% in using 5-folds cross-validation applied to a balanced dataset of 3104 samples. These samples are extracted from 16 subjects where eight are healthy and eight are epileptic subjects using a sliding frame of size of 100 samples-points with a step-size of 2 sample-points.

IEEE Journal of Biomedical and Health Informatics

Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST) in collaboration with King Abdulaziz City for Science and Technology (KACST) and King Saud University (KSU).

Abderrazak Chahid
Abderrazak Chahid
PhD in Artificial Intelligence

My research interests include feature extraction, real-time implementation of smart decision making systems.