title: 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 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.
authors.affiliation: Department of Computer Engineering and Informatics, University of Patras, Patras, Greece. ezachar@upatras.gr.|Center for Visual Computing, CentraleSupélec/Galen Team, INRIA, Paris, France. ezachar@upatras.gr.
authors.forename: Evangelia I
authors.identifier: http://orcid.org/0000-0001-8228-0437
authors.initials: EI
authors.lastname: Zacharaki
authors.affiliation: Department of Computer Engineering and Informatics, University of Patras, Patras, Greece.
authors.forename: Iosif
authors.initials: I
authors.lastname: Mporas
authors.affiliation: St. Luke's Hospital, Thessaloniki, Greece.
authors.forename: Kyriakos
authors.initials: K
authors.lastname: Garganis
authors.affiliation: Department of Computer Engineering and Informatics, University of Patras, Patras, Greece.
authors.forename: Vasileios
authors.initials: V
authors.lastname: Megalooikonomou
keywords: Dimensionality reduction; Epilepsy; Manifold learning; Pattern recognition; Spike detection