dc.contributor.author | Mporas, Iosif | |
dc.date.accessioned | 2017-06-29T11:07:44Z | |
dc.date.available | 2017-06-29T11:07:44Z | |
dc.date.issued | 2016-06-01 | |
dc.identifier.citation | Mporas , I 2016 , ' Spike pattern recognition by supervised classification in low dimensional embedding space ' , Brain Informatics , vol. 3 , no. 2 , pp. 73-83 . https://doi.org/10.1007/s40708-016-0044-4 | |
dc.identifier.issn | 2198-4018 | |
dc.identifier.uri | http://hdl.handle.net/2299/18643 | |
dc.description | © The Author(s) 2016. This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution License 4.0, (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. | |
dc.description.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. | en |
dc.format.extent | 11 | |
dc.format.extent | 1828337 | |
dc.language.iso | eng | |
dc.relation.ispartof | Brain Informatics | |
dc.subject | spike detection | |
dc.subject | epilepsyPATTERN RECOGNITION | |
dc.subject | MANIFOLD LEARNING | |
dc.subject | DIMENSIONALITY REDUCTION | |
dc.title | Spike pattern recognition by supervised classification in low dimensional embedding space | en |
dc.contributor.institution | School of Engineering and Technology | |
dc.description.status | Peer reviewed | |
rioxxterms.versionofrecord | 10.1007/s40708-016-0044-4 | |
rioxxterms.type | Journal Article/Review | |
herts.preservation.rarelyaccessed | true | |