New cardiomechanic pacing lead sensor based on high frequency parameters: experimental studies in sheep.

UNLABELLED: Pacing Lead as a High Frequency Cardiomechanic Sensor. INTRODUCTION: The purpose of this study was to investigate the possibility of detecting and quantifying ventricular contraction in sheep utilizing the cardiomechanic sensor based upon the high frequency (HF) parameters measurements on bipolar cardiac pacing leads. Measurement of the HF reflection coefficient yields the lead-bending signal (LBS) caused by myocardial contraction. The correlation between the lead-bending acceleration (LBA) expressed as the rate of rise of LBS and LV dP/dt should reveal that LBS may be utilized as a cardiomechanic sensor in implantable cardiac electrotherapy devices. METHODS AND RESULTS: We implanted 3 different pacing leads and tested the measurement system in 9 sheep (42 ± 6 kg) at baseline and during acute hemodynamic intervention with dobutamine infusion and tachycardia induced by VVI pacing at 200 bpm. A stable, consistent, and reproducible LBS was obtained in all sheep during the implantation procedure and 4 months after the implantation during different experimental conditions that included hemodynamic interventions. The dependence between LBAmax and LV dP/dtmax was found to be statistically significant and with high Pearson's correlation coefficient (r = 0.855, P <0.001). We could also observe the hemodynamic deterioration caused by fast ventricular pacing with the decrease of LV dp/dt and LBA compared with sinus rhythm. CONCLUSION: This study confirms the feasibility and efficacy of the hemodynamic sensor based upon HF lead parameters. Moreover, it was demonstrated that LBAmax is highly correlated to the ventricular contractility and, therefore, can be efficiently used as a hemodynamic and cardiomechanic sensor. (J Cardiovasc Electrophysiol, Vol. 24, pp. 338-346, March 2013).

A simplified 3D model of whole heart electrical activity and 12-lead ECG generation.

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.

Influence of active dendrites on firing patterns in a retinal ganglion cell model.

Active regional conductances and inhomogeneous distribution of membrane ionic channels in dendrites influence the integration of synaptic inputs in cortical neurons. How these properties shape the response properties of retinal ganglion cells (RGC) in the mammalian retina has remained largely unexplored. In this study, we used a morphologically-realistic RGC computational model to study how active dendritic properties contribute to neural behaviors. Our simulations suggest that the dendritic distribution of voltage-gated ionic channels strongly influences RGC firing patterns, indicating their important contribution to neuronal function.

ECG-based prediction of atrial fibrillation development following coronary artery bypass grafting.

In patients undergoing coronary artery bypass grafting (CABG) surgery, post-operative 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 and prediction of post-operative AF is optimal in the period between 24 and 48 h following CABG.