Efficient modeling of ECG waves for morphology tracking

• Published in: 2009 36th Annual Computers in Cardiology Conference (CinC) pp. 313 - 316
• Main Authors: Dubois, R, Roussel, P, Vaglio, M, Extramiana, F, Badilini, F, Maison-Blanche, P, Dreyfus, G
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-09-2009
• Subjects: Algorithm design and analysis; Biomedical signal processing; Electrocardiography; Iterative algorithms; Libraries; Morphology; Signal analysis; Signal processing algorithms; Vectors
• ISBN: 9781424472819; 1424472814
• ISSN: 0276-6574; 2325-8853

We propose a new approach to fully automatic ECG wave extraction and morphology tracking. It is based on Generalized Orthogonal Forward Regression (GOFR), which allows decomposing a one-dimensional signal into a set of appropriate parameterized functions. Two applications of GOFR to ECG modeling are presented. First, in order to delineate ECG characteristic waves, we make use of a specific function, called the Gaussian Mesa function (GMF). Secondly, we track the evolution of the T-wave morphology by introducing a Bi-Gaussian function (BGF). The approach was validated on three experimental settings; the results confirm that the combination of GOFR and of an appropriate parametric function is remarkably efficient for ECG wave modeling.