title: Model-based fiducial points extraction for baseline wandered electrocardiograms.
abstract: A fast algorithm based on the nonlinear dynamical model for the electrocardiogram (ECG) is presented for the precise extraction of the characteristic points of these signals with baseline drift. Using the adaptive bionic wavelet transform, the baseline wander is removed efficiently. In fact by the means of the bionic wavelet transform, the resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential, which results in a better baseline wander cancellation. At the next step the parameters of the model are chosen to have the least square error with the original ECG. Determining the precise position of the waveforms of an ECG signal with baseline wander is complicated due to the varying amplitudes of its waveforms, the ambiguous and changing form of the complex and the unknown drift. A model-based approach handles these complications, therefore a method based on this concept has been developed and the fiducial points are accurately detected using the center and spread parameters of Gaussian-functions of the model. Simulation results show that the proposed method has an average sensitivity of 99.58%, average detection accuracy of 99.64%, and specificity of 100%.
authors.affiliation: Biomedical Signal and Image Processing Laboratory (BiSIPL), Electrical Engineering Department, Sharif University of Technology, Tehran, Iran. osayadi@ee.sharif.edu
authors.forename: Omid
authors.initials: O
authors.lastname: Sayadi
authors.forename: Mohammad B
authors.initials: MB
authors.lastname: Shamsollahi
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D016477:Artifacts; D001185:Artificial Intelligence; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*