title: ECG data compression using Jacobi polynomials.
abstract: Data compression is a frequent signal processing operation applied to ECG. We present here a method of ECG data compression utilizing Jacobi polynomials. ECG signals are first divided into blocks that match with cardiac cycles before being decomposed in Jacobi polynomials bases. Gauss quadratures mechanism for numerical integration is used to compute Jacobi transforms coefficients. Coefficients of small values are discarded in the reconstruction stage. For experimental purposes, we chose height families of Jacobi polynomials. Various segmentation approaches were considered. We elaborated an efficient strategy to cancel boundary effects. We obtained interesting results compared with ECG compression by wavelet decomposition methods. Some propositions are suggested to improve the results.
authors.affiliation: Electrical Engineering Department, IUT FOTSO Victor, University of Dschang, Cameroon. dtchiot@yahoo.fr
authors.forename: Daniel
authors.initials: D
authors.lastname: Tchiotsop
authors.forename: Didier
authors.initials: D
authors.lastname: Wolf
authors.forename: Valérie
authors.initials: V
authors.lastname: Louis-Dorr
authors.forename: René
authors.initials: R
authors.lastname: Husson
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D044962:Data Compression; D003627:Data Interpretation, Statistical; D004562:Electrocardiography / Q000295:instrumentation* / Q000379:methods; D004867:Equipment Design; D006801:Humans; D008433:Mathematics; D015233:Models, Statistical; D008962:Models, Theoretical; D012815:Signal Processing, Computer-Assisted