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 |