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.
authors.affiliation: LAPS, University of Bordeaux I, 351-cours de la Libération, 33405 Talence, France. kleydis.suarez@laps.ims-bordeaux.fr
authors.forename: Kleydis V
authors.initials: KV
authors.lastname: Suárez
authors.forename: Jesus C
authors.initials: JC
authors.lastname: Silva
authors.forename: Yannick
authors.initials: Y
authors.lastname: Berthoumieu
authors.forename: Pedro
authors.initials: P
authors.lastname: Gomis
authors.forename: Mohamed
authors.initials: M
authors.lastname: Najim
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology*; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity