Search for: arrhythmias--cardiac
Article IEEE Transactions on Biomedical Engineering ; Volume 55, Issue 1 , 2008 , Pages 347-351 ; 00189294 (ISSN) ; Shamsollahi, M. B ; Sharif University of Technology
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...
Article Computer Methods and Programs in Biomedicine ; Volume 157 , 2018 , Pages 129-136 ; 01692607 (ISSN) ; Montazeri Ghahjaverestan, N ; Shamsollahi, M. B ; Jutten, C ; Sharif University of Technology
Elsevier Ireland Ltd 2018
In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regressive models. In the proposed method, a discrete state variable called “switch” is considered that affects only the observation equations. We denote a mode as a specific observation equation and switch changes between 7 modes and corresponds to different segments of an ECG beat. At each time instant, the probability of each mode is calculated and...
Life-threatening arrhythmia verification in ICU patients using the joint cardiovascular dynamical model and a bayesian filter, Article IEEE Transactions on Biomedical Engineering ; Volume 58, Issue 10 PART 1 , 2011 , Pages 2748-2757 ; 00189294 (ISSN) ; Shamsollahi, M. B ; Sharif University of Technology
In this paper, a novel nonlinear joint dynamical model is presented, which is based on a set of coupled ordinary differential equations of motion and a Gaussian mixture model representation of pulsatile cardiovascular (CV) signals. In the proposed framework, the joint interdependences of CV signals are incorporated by assuming a unique angular frequency that controls the limit cycle of the heart rate. Moreover, the time consequence of CV signals is controlled by the same phase parameter that results in the space dimensionality reduction. These joint equations together with linear assignments to observation are further used in the Kalman filter structure for estimation and tracking. Moreover,...