Spike pattern recognition by supervised classification in low dimensional embedding space

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    Abstract

    Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.
    Original languageEnglish
    Pages (from-to)73-83
    Number of pages11
    JournalBrain Informatics
    Volume3
    Issue number2
    Early online date16 Mar 2016
    DOIs
    Publication statusPublished - 1 Jun 2016

    Keywords

    • spike detection
    • epilepsyPATTERN RECOGNITION
    • MANIFOLD LEARNING
    • DIMENSIONALITY REDUCTION

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