A semi-automatic feature detection algorithm for hemodynamic signals using curvature-based feature extraction.
Specific features of hemodynamic signals are invaluable for elucidating ventricular and vascular function. A semi-automatic algorithm is presented that enables accurate detection of any feature in any hemodynamic signal, using feature extraction from local maxima and minima in signal curvature. A particular feature is selected manually in the first beat and then detected automatically in subsequent beats.
Mynard
Jonathan P
JP
Heart Research Group, Murdoch Children's Research Institute, Melbourne, Australia. jonathan.mynard@mcri.edu.au
Penny
Daniel J
DJ
Smolich
Joseph J
JJ
D000465:Algorithms; D001011:Aorta / Q000473:pathology; D001331:Automation; D003201:Computers; D001330:Electronic Data Processing; D004867:Equipment Design; D006439:Hemodynamics; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D011312:Pressure; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013997:Time Factors