We present a computationally efficient three-dimensional bidomain model of torso-embedded whole heart electrical activity, with spontaneous initiation of activation in the sinoatrial node, incorporating a specialized conduction system with heterogeneous action potential morphologies throughout the heart. The simplified geometry incorporates the whole heart as a volume source, with heart cavities, lungs, and torso as passive volume conductors. We placed four surface electrodes at the limbs of the torso: V R, V L, V F and V GND and six electrodes on the chest to simulate the Einthoven, Goldberger-augmented and precordial leads of a standard 12-lead system. By placing additional seven electrodes at the appropriate torso positions, we were also able to calculate the vectorcardiogram of the Frank lead system. Themodel was able to simulate realistic electrocardiogram (ECG) morphologies for the 12 standard leads, orthogonal X, Y, and Z leads, as well as the vectorcardiogram under normal and pathological heart states. Thus, simplified and easy replicable 3D cardiac bidomain model offers a compromise between computational load and model complexity and can be used as an investigative tool to adjust cell, tissue, and whole heart properties, such as setting ischemic lesions or regions of myocardial infarction, to readily investigate their effects on whole ECG morphology.
In patients undergoing coronary artery bypass grafting (CABG) surgery, postoperative atrial fibrillation (AF) occurs with a prevalence of up to 40%. The highest incidence is seen between the second and third day after the operation. Following cardiac surgery AF may cause various complications such as hemodynamic instability, heart attack and cerebral or other thromboembolisms. AF increases morbidity, duration and expense of medical treatments. This study aims at identifying patients at high risk of post-operative AF. Early prediction of AF would provide timely prophylactic treatment and would reduce the incidence of arrhythmia. Patients at low risk of post-operative AF could be excluded on the basis of the contraindications of anti-arrhythmic drugs. The study included 50 patients in whom lead II electrocardiograms were continuously recorded for 48 h following CABG. Univariate statistical analysis was used in the search for signal features that could predict AF. The most promising ones identified were P wave duration, RR interval duration and PQ segment level. On the basis of these, a nonlinear multivariate prediction model was made by deploying a classification tree. The prediction accuracy was found to increase over time. At 48 h following CABG, the measured best smoothed sensitivity was 84.8% and the specificity 85.4%. The positive and negative predictive values were 72.7% and 92.8%, respectively, and the overall accuracy was 85.3%. With regard to the prediction accuracy, the risk assessment 4 Author to whom any correspondence should be addressed. and prediction of post-operative AF is optimal in the period between 24 and 48 h following CABG.
The aim of this study was the development of a geometrically simple and highly computationally-efficient two dimensional (2D) biophysical model of whole heart electrical activity, incorporating spontaneous activation of the sinoatrial node (SAN), the specialized conduction system, and realistic surface ECG morphology computed on the torso. The FitzHugh-Nagumo (FHN) equations were incorporated into a bidomain finite element model of cardiac electrical activity, which was comprised of a simplified geometry of the whole heart with the blood cavities, the lungs and the torso as an extracellular volume conductor. To model the ECG, we placed four electrodes on the surface of the torso to simulate three Einthoven leads V I , V II and V III from the standard 12-lead system. The 2D model was able to reconstruct ECG morphology on the torso from action potentials generated at various regions of the heart, including the sinoatrial node, atria, atrioventricular node, His bundle, bundle branches, Purkinje fibers, and ventricles. Our 2D cardiac model offers a good compromise between computational load and model complexity, and can be used as a first step towards three dimensional (3D) ECG models with more complex, precise and accurate geometry of anatomical structures, to investigate the effect of various cardiac electrophysiological parameters on ECG morphology.
InECG signals processing it is sometimes necessary to determine the timing of different ECG segments in real time, i.e. in the shortest time after they appeared in the signal. A typical example is P-wave synchronized pacing (heart stimulation synchronized with the P-wave). We used multistage methodology enabled by Wavelet Transform to delineate the ECG signal and develop a sensitive (98,5%) and reliable P-wave detector.
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