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    Feature Generation and Dimensionality Reduction using the Discrete Spectrum of the Schrödinger Operator for Epileptic Spikes Detection

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    EMBC2019_Epilepsy_spikes_detection_using_SCSA_preprint.pdf
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    Description:
    Accepted manuscript
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    Type
    Conference Paper
    Authors
    Chahid, Abderrazak cc
    Alotaiby, Turky Nayef
    Alshebeili, Saleh
    Laleg-Kirati, Taous-Meriem cc
    KAUST Department
    Electrical Engineering Program
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    KAUST Grant Number
    BAS/1/1627-01-01
    Date
    2019-10-08
    Permanent link to this record
    http://hdl.handle.net/10754/660368
    
    Metadata
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    Abstract
    Epilepsy 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.
    Citation
    Chahid, 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
    Sponsors
    Research 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).
    Publisher
    IEEE
    Conference/Event name
    2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
    DOI
    10.1109/EMBC.2019.8856702
    Additional Links
    https://ieeexplore.ieee.org/document/8856702/
    https://ieeexplore.ieee.org/document/8856702/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8856702
    ae974a485f413a2113503eed53cd6c53
    10.1109/EMBC.2019.8856702
    Scopus Count
    Collections
    Conference Papers; Electrical Engineering Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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