QuPWM: Feature Extraction Method for Epileptic Spike Classification

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 10; pp. 2814 - 2824
• Main Authors: Chahid, Abderrazak, Albalawi, Fahad, Alotaiby, Turky Nayef, Al-Hameed, Majed Hamad, Alshebeili, Saleh, Laleg-Kirati, Taous-Meriem
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Brain - physiopathology; Classification; Electroencephalography - methods; Epilepsy; Epilepsy - diagnosis; epileptic spike detection; Fast Fourier transformations; Feature extraction; Fourier transforms; Humans; Learning algorithms; Machine Learning; Magnetic noise; Magnetic shielding; Magnetoencephalography; Magnetoencephalography (MEG); Magnetometers; Neurological diseases; position weight matrix (PWM); Pulse width modulation; Quantization (signal); Seizures; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Spikes; Visual signals
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2020.2972286

Epilepsy is a neurological disorder ranked as the second most serious neurological disease known to humanity, after stroke. Inter-ictal spiking is an abnormal neuronal discharge after an epileptic seizure. This abnormal activity can originate from one or more cranial lobes, often travels from one lobe to another, and interferes with normal activity from the affected lobe. The common practice for Inter-ictal 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 article focuses on using machine learning for epileptic spikes classification in magnetoencephalography (MEG) signals. First, we used the Position Weight Matrix (PWM) method combined with a uniform quantizer to generate useful features from time domain and frequency domain through a Fast Fourier Transform (FFT) of the framed raw MEG signals. Second, the extracted features are fed to standard classifiers for inter-ictel spikes classification. The proposed technique shows great potential in spike classification and reducing the feature vector size. Specifically, the proposed technique achieved average sensitivity up to 87% and specificity up to 97% using 5-folds cross-validation applied to a balanced dataset. These samples are extracted from nine epileptic subjects using a sliding frame of size 95 samples-points with a step-size of 8 sample-points.