Feature Generation and Dimensionality Reduction using the Discrete Spectrum of the Schrödinger Operator for Epileptic Spikes Detection
KAUST DepartmentElectrical Engineering Program
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberBAS/1/1627-01-01
Permanent link to this recordhttp://hdl.handle.net/10754/660368
MetadataShow full item record
AbstractEpilepsy is a neurological disorder classified as the second most serious neurological disease known to humanity, after stroke. Magnetoencephalography (MEG) is performed to localize the epileptogenic zone in the brain. However, the detection of epileptic spikes requires the visual assessment of long MEG recordings. This task is time-consuming and might lead to wrong decisions. Therefore, the introduction of effective machine learning algorithms for the quick and accurate epileptic spikes detection from MEG recordings would improve the clinical diagnosis of the disease. The efficiency of machine learning based algorithms requires a good characterization of the signal by extracting pertinent features. In this paper, we propose new sets of features for MEG signals. These features are based on a Semi-Classical Signal Analysis (SCSA) method, which allows a good characterization of peak shaped signals. Moreover, this method improves the spike detection accuracy and reduces the feature vector size. We could achieve up to 93.68% and 95.08% in average sensitivity and specificity, respectively. We used the 5-folds cross-validation applied to a balanced dataset of 3104 frames, extracted from eight healthy and eight epileptic subjects with a frame size of 100 samples with a step size of 2 samples, using Random Forest (RF) classifier.
CitationChahid, A., Alotaiby, T. N., Alshebeili, S., & Laleg-Kirati, T.-M. (2019). Feature Generation and Dimensionality Reduction using the Discrete Spectrum of the Schrödinger Operator for Epileptic Spikes Detection. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2019.8856702
SponsorsResearch reported in this publication was supported by King Abdullah University of Science and Technology (KAUST)-Base Research Fund (BAS/1/1627-01-01), in collaboration with King Abdulaziz City for Science and Technology (KACST) and King Saud University (KSU).
Conference/Event name2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)