Neural network based classification of laser-Doppler flowmetry signals

Panagiotidis, NG; Delopoulos, A; Kollias, SD

dc.contributor.author	Panagiotidis, NG	en
dc.contributor.author	Delopoulos, A	en
dc.contributor.author	Kollias, SD	en
dc.date.accessioned	2014-03-01T02:41:02Z
dc.date.available	2014-03-01T02:41:02Z
dc.date.issued	1994	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30335
dc.subject	Frequency Domain	en
dc.subject	laser doppler flowmetry	en
dc.subject	Multilayer Perceptron	en
dc.subject	Patient Monitoring	en
dc.subject	Blood Flow	en
dc.subject	Neural Network	en
dc.subject.other	Correlation theory	en
dc.subject.other	Doppler effect	en
dc.subject.other	Flowmeters	en
dc.subject.other	Frequency domain analysis	en
dc.subject.other	Hemodynamics	en
dc.subject.other	Noninvasive medical procedures	en
dc.subject.other	Patient monitoring	en
dc.subject.other	Signal filtering and prediction	en
dc.subject.other	Spectrum analysis	en
dc.subject.other	Laser Doppler flowmetry	en
dc.subject.other	Neural networks	en
dc.title	Neural network based classification of laser-Doppler flowmetry signals	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/NNSP.1994.365994	en
heal.identifier.secondary	http://dx.doi.org/10.1109/NNSP.1994.365994	en
heal.publicationDate	1994	en
heal.abstract	Laser Doppler flowmetry is a most advantageous technique for non-invasive patient monitoring. Based on the Doppler principle, signals corresponding to blood flow are generated, and metrics corresponding to healthy vs. patient samples are extracted. A neural-network based classifier for these metrics is proposed. The signals are initially filtered, and transformed into the frequency domain through third-order correlation and bispectrum estimation. The pictorial representation of the correlations is subsequently routed into a neural network based MLP classifier, which is described in detail. Finally, experimental results demonstrating the efficiency of the proposed scheme are prted.	en
heal.publisher	IEEE, Piscataway, NJ, United States	en
heal.journalName	Neural Networks for Signal Processing - Proceedings of the IEEE Workshop	en
dc.identifier.doi	10.1109/NNSP.1994.365994	en
dc.identifier.spage	709	en
dc.identifier.epage	715	en