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    QuPWM: Feature Extraction Method for MEG Epileptic Spike Detection

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    Name:
    Journal2019_PWM_Epilepsy_spikes_Detection (2).pdf
    Size:
    2.819Mb
    Format:
    PDF
    Description:
    Preprint
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    Type
    Preprint
    Authors
    Chahid, Abderrazak cc
    Albalawi, Fahad
    Alotaiby, Turky Nayef
    Al-Hameed, Majed Hamad
    Alshebeili, Saleh
    Laleg-Kirati, Taous-Meriem cc
    KAUST Department
    Computational Bioscience Research Center
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering
    Electrical Engineering Program
    KAUST Grant Number
    BAS/1/1627-01-01
    Date
    2019-07-03
    Permanent link to this record
    http://hdl.handle.net/10754/655961
    
    Metadata
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    Summary

    This record has been merged with an existing record at: http://hdl.handle.net/10754/661489.

    Abstract
    Epilepsy is a neurological disorder classified as the second most seriousneurological disease known to humanity, after stroke. Localization of theepileptogenic zone is an important step for epileptic patient treatment, whichstarts with epileptic spike detection. The common practice for spike detectionof brain signals is via visual scanning of the recordings, which is asubjective and a very time-consuming task. Motivated by that, this paperfocuses on using machine learning for automatic detection of epileptic spikesin magnetoencephalography (MEG) signals. First, we used the Position WeightMatrix (PWM) method combined with a uniform quantizer to generate usefulfeatures. Second, the extracted features are classified using a Support VectorMachine (SVM) for the purpose of epileptic spikes detection. The proposedtechnique shows great potential in improving the spike detection accuracy andreducing the feature vector size. Specifically, the proposed technique achievedaverage accuracy up to 98\% in using 5-folds cross-validation applied to abalanced dataset of 3104 samples. These samples are extracted from 16 subjectswhere eight are healthy and eight are epileptic subjects using a sliding frameof size of 100 samples-points with a step-size of 2 sample-points
    Sponsors
    Acknowledgment: 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). Funding: This research project has been funded 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
    arXiv
    arXiv
    1907.02596
    Additional Links
    https://arxiv.org/pdf/1907.02596
    https://github.com/EMANG-KAUST/QuPWM
    Collections
    Preprints; Electrical Engineering Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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