A Semi-Automatic Feature Detection Algorithm for Hemodynamic Signals using Curvature-Based Feature Extraction

Author

Mynard, J.P. ; Penny, D.J. ; Smolich, J.J.

Author_Institution

Murdoch Children´s Res. Inst., Melbourne

fYear

2007

fDate

22-26 Aug. 2007

Firstpage

1691

Lastpage

1694

Abstract

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.

Keywords

cardiology; feature extraction; haemodynamics; medical signal detection; medical signal processing; feature detection; feature extraction; hemodynamic signals; semiautomatic algorithm; signal curvature; vascular function; ventricular function; Algorithm design and analysis; Australia; Computer vision; Detection algorithms; Feature extraction; Frequency; Graphical user interfaces; Heart; Hemodynamics; Signal analysis; Algorithms; Aorta; Automatic Data Processing; Automation; Computers; Equipment Design; Hemodynamics; Humans; Image Processing, Computer-Assisted; Models, Theoretical; Pattern Recognition, Automated; Pressure; Signal Processing, Computer-Assisted; Software; Time Factors;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE

Conference_Location

Lyon

ISSN

1557-170X

Print_ISBN

978-1-4244-0787-3

Type

conf

DOI

10.1109/IEMBS.2007.4352634

Filename

4352634