Evaluation of a signal analysis system under simulated noise conditions

dc.contributor.author	Καλατζής, Ιωάννης	el
dc.contributor.author	Νίνος, Κωνσταντίνος Δ.	el
dc.contributor.author	Γεωργιάδης, Παντελής	el
dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.date.accessioned	2015-01-29T13:15:07Z
dc.date.issued	2015-01-29
dc.identifier.uri	http://hdl.handle.net/11400/5064
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	Pattern recognition
dc.subject	Evoked potentials (Electrophysiology)
dc.subject	Αναγνώριση προτύπων
dc.subject	Δυνατότητες για τη συγκεκριμένη εκδήλωση
dc.title	Evaluation of a signal analysis system under simulated noise conditions	en
heal.type	conferenceItem
heal.generalDescription	Proceedings of the 2nd International Conference on Experiments/Process/System Modelling/Simulation & Optimization (2nd IC-EpsMsO), (CD-ROM)	en
heal.classification	Medicine
heal.classification	Medical technology
heal.classification	Ιατρική
heal.classification	Ιατρικά όργανα και εξοπλισμός
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://skos.um.es/unescothes/C02465
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Ιατρικά όργανα και εξοπλισμός
heal.keywordURI	http://skos.um.es/unescothes/C02924
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85046028
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2007
heal.bibliographicCitation	Kalatzis, I., Ninos, K., Georgiadis, P., Ventouras, E. and Cavouras, D. (2007). Evaluation of a signal analysis system under simulated noise conditions. In the 2nd International Conference on Experiments/Process/System Modelling/Simulation & Optimization. University of Patras: Athens, 2007.	en
heal.abstract	Purpose: To examine the robustness of a pattern recognition system under the influence of noise in Event Related Potentials (ERP) signals in discriminating controls from patients. Material and Methods: ERP recordings were simulated by generating two series of signals, based on real signal templates from normal controls and patients, with various levels of added-on Gaussian noise. From the resulting signals, a number of waveform characteristic quantities were calculated and they were used as input to an ensemble classification structure, which consisted of three different classifiers, namely the Bayesian classifier, the k-Nearest Neighbor (kNN) and the Probabilistic Neural Network (PNN), and following the majority vote rule. Results: The classification accuracies of individual classifiers were over 80% for the PNN and over 75% for the kNN and the Bayesian. The ensemble structure improved classification precision resulting in an overall accuracy of over 87% for all noise levels tested. Conclusion: Results provide an estimation of the robustness of the developed ensemble classification scheme, which may be of value to the clinician, given that ERP signals are usually corrupted by noise in clinical practice.	en
heal.publisher	[χ.ό.]	el
heal.fullTextAvailability	true
heal.conferenceName	International Conference on Experiments/Process/System Modelling/Simulation & Optimization	en
heal.conferenceItemType	full paper