Paper Info
Title
ECG Beat Detection Using a Geometrical Matching Approach.
Abstract
In the framework of the electrocardiography (ECG) signals, this paper describes an original approach to identify heartbeat morphologies and to detect R-wave events. The proposed approach is based on a "geometrical matching rule evaluated using a decision function in a local moving-window procedure. The decision function is a normalized measurement of a similarity criterion comparing the windowed input signal with the reference beat-pattern into a nonlinear-curve space. A polynomial expansion model describes the reference pattern. For the curve space, an algebraic-fitting distance is built according to the canonical equation of the unit circle. The geometrical matching approach operates in two stages, i.e., training and detection ones. In the first stage, a learning-method based on genetic algorithms allows us estimating the decision function from training beat-pattern. In the second stage, a level-detection algorithm evaluates the decision function to establish the threshold of similarity between the reference pattern and the input signal. Finally, the findings for the MIT-BIH Arrhythmia Database present about 98% of sensitivity and 99% of positive predictivity for the R-waves detection, using low-order polynomial models.
Year
DOI
Venue
2007
10.1109/TBME.2006.889944
IEEE Trans. Biomed. Engineering
Keywords
Field
DocType
databases,artificial intelligence,algorithms,polynomials,genetic algorithm,signal processing,learning artificial intelligence,polynomial expansion,nonlinear equations,morphology,genetic algorithms
Signal processing,Heartbeat,Normalization (statistics),Polynomial,Computer science,Algorithm,Unit circle,Beat detection,Polynomial expansion,Genetic algorithm
Journal
Volume
Issue
ISSN
54
4
0018-9294
Citations 
PageRank 
References 
15
2.22
8
Authors
5
Name
Order
Citations
PageRank
Kleydis V. Suarez1152.22
Jesus C. Silva2152.22
Y. Berthoumieu338951.66
Pedro Gomis4185.41
Mohamed Najim514932.29