Spike pattern recognition by supervised classification in low dimensional embedding space

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 (SVM) in a low dimensional space on which the original waveforms are embedded by Locality Preserving Projections (LPP). 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.
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Contributor : Evangelia Zacharaki <>
Submitted on : Thursday, September 1, 2016 - 11:00:31 PM
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Evangelia I. Zacharaki, Iosif Mporas, Kyriakos Garganis, Vasileios Megalooikonomou. Spike pattern recognition by supervised classification in low dimensional embedding space. Brain Informatics, Springer, 2016, 〈10.1007/s40708-016-0044-4〉. 〈hal-01359155〉



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