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Neural network based classification of laser-Doppler flowmetry signals

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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


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