Importance of cardiac-synchronized vagus nerve stimulation parameters on the provoked chronotropic response for different levels of cardiac innervation.

Introduction: The influence of vagus nerve stimulation (VNS) parameters on provoked cardiac effects in different levels of cardiac innervation is not well understood yet. This study examines the effects of VNS on heart rate (HR) modulation across a spectrum of cardiac innervation states, providing data for the potential optimization of VNS in cardiac therapies. Materials and Methods: Utilizing previously published data from VNS experiments on six sheep with intact innervation, and data of additional experiments in five rabbits post bilateral rostral vagotomy, and four isolated rabbit hearts with additionally removed sympathetic influences, the study explored the impact of diverse VNS parameters on HR. Results: Significant differences in physiological threshold charges were identified across groups: 0.09 ± 0.06 µC for intact, 0.20 ± 0.04 µC for vagotomized, and 9.00 ± 0.75 µC for isolated hearts. Charge was a key determinant of HR reduction across all innervation states, with diminishing correlations from intact (r = 0.7) to isolated hearts (r = 0.44). An inverse relationship was observed for the number of pulses, with its influence growing in conditions of reduced innervation (intact r = 0.11, isolated r = 0.37). Frequency and stimulation delay showed minimal correlations (r < 0.17) in all conditions. Conclusion: Our study highlights for the first time that VNS parameters, including stimulation intensity, pulse width, and pulse number, crucially modulate heart rate across different cardiac innervation states. Intensity and pulse width significantly influence heart rate in innervated states, while pulse number is key in denervated states. Frequency and delay have less impact impact across all innervation states. These findings suggest the importance of customizing VNS therapy based on innervation status, offering insights for optimizing cardiac neuromodulation.

Decoding cardiac reinnervation from cardiac autonomic markers: A mathematical model approach.

BACKGROUND: Although cardiac autonomic markers (CAMs) are commonly used to assess cardiac reinnervation in heart-transplant patients, their relationship to the degree of sympathetic and vagal cardiac reinnervation is not well understood yet. To study this relationship, we applied a mathematical model of the cardiovascular system and its autonomic control. METHODS: By simulating varying levels of sympathetic and vagal efferent sinoatrial reinnervation, we analyzed the induced changes in CAMs including resting heart rate (HR), bradycardic and tachycardic HR response to Valsalva maneuver, root mean square of successive differences between normal heartbeats (RMSSD), low-frequency (LF), high-frequency (HF), and total spectral power (TSP). RESULTS: For assessment of vagal cardiac reinnervation levels >20%, resting HR (ρ = 0.99, p < 0.05), RMSSD (ρ = 0.97, p < 0.05), and TSP (ρ = 0.96, p < 0.05) may be equally suitable as HF-power (ρ = 0.97, p < 0.05). To assess sympathetic reinnervation, LF/HF ratio (ρ = 0.87, p < 0.05) and tachycardic response to Valsalva maneuver (ρ = 0.9, p < 0.05) may be more suitable than LF-power (ρ = 0.77, p < 0.05). CONCLUSIONS: Our model reports mechanistic relationships between CAMs and levels of efferent autonomic sinoatrial reinnervation. The results indicate differences in the suitability of these markers to assess vagal and sympathetic reinnervation. Although our analysis is purely conceptual, the developed model can help to gain important insights into the genesis of CAMs and their relationship to efferent sinoatrial reinnervation and, thus, provide indications for clinical study evaluation.

Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges.

Training deep neural network classifiers for electrocardiograms (ECGs) requires sufficient data. However, imbalanced datasets pose a major problem for the training process and hence data augmentation is commonly performed. Generative adversarial networks (GANs) can create synthetic ECG data to augment such imbalanced datasets. This review aims at identifying the present literature concerning synthetic ECG signal generation using GANs to provide a comprehensive overview of architectures, quality evaluation metrics, and classification performances. Thirty publications from the years 2019 to 2022 were selected from three separate databases. Nine publications used a quality evaluation metric neglecting classification, eleven performed a classification but omitted a quality evaluation metric, and ten publications performed both. Twenty different quality evaluation metrics were observed. Overall, the classification performance of databases augmented with synthetically created ECG signals increased by 7 % to 98 % in accuracy and 6 % to 97 % in sensitivity. In conclusion, synthetic ECG signal generation using GANs represents a promising tool for data augmentation of imbalanced datasets. Consistent quality evaluation of generated signals remains challenging. Hence, future work should focus on the establishment of a gold standard for quality evaluation metrics for GANs.

A novel ex-vivo isolated rabbit heart preparation to explore the cardiac effects of cervical and cardiac vagus nerve stimulation.

The cardiac responses to vagus nerve stimulation (VNS) are still not fully understood, partly due to uncontrollable confounders in the in-vivo experimental condition. Therefore, an ex-vivo Langendorff-perfused rabbit heart with intact vagal innervation is proposed to study VNS in absence of cofounding anesthetic or autonomic influences. The feasibility to evoke chronotropic responses through electrical stimulation ex-vivo was studied in innervated isolated rabbit hearts (n = 6). The general nerve excitability was assessed through the ability to evoke a heart rate (HR) reduction of at least 5 bpm (physiological threshold). The excitability was quantified as the charge needed for a 10-bpm HR reduction. The results were compared to a series of in-vivo experiments rabbits (n = 5). In the ex-vivo isolated heart, the baseline HR was about 20 bpm lower than in-vivo (158 ± 11 bpm vs 181 ± 19 bpm). Overall, the nerve remained excitable for about 5 h ex-vivo. The charges required to reduce HR by 5 bpm were 9 ± 6 µC and 549 ± 370 µC, ex-vivo and in-vivo, respectively. The charges needed for a 10-bpm HR reduction, normalized to the physiological threshold were 1.78 ± 0.8 and 1.22 ± 0.1, in-vivo and ex-vivo, respectively. Overall, the viability of this ex-vivo model to study the acute cardiac effects of VNS was demonstrated.

Closed-loop vagus nerve stimulation for heart rate control evaluated in the Langendorff-perfused rabbit heart.

Persistent sinus tachycardia substantially increases the risk of cardiac death. Vagus nerve stimulation (VNS) is known to reduce the heart rate, and hence may be a non-pharmacological alternative for the management of persistent sinus tachycardia. To precisely regulate the heart rate using VNS, closed-loop control strategies are needed. Therefore, in this work, we developed two closed-loop VNS strategies using an in-silico model of the cardiovascular system. Both strategies employ a proportional-integral controller that operates on the current amplitude. While one control strategy continuously delivers stimulation pulses to the vagus nerve, the other applies bursts of stimuli in synchronization with the cardiac cycle. Both were evaluated in Langendorff-perfused rabbit hearts (n = 6) with intact vagal innervation. The controller performance was quantified by rise time (Tr), steady-state error (SSE), and percentual overshoot amplitude (%OS). In the ex-vivo setting, the cardiac-synchronized variant resulted in Tr = 10.7 ± 4.5 s, SSE = 12.7 ± 9.9 bpm and %OS = 5.1 ± 3.6% while continuous stimulation led to Tr = 10.2 ± 5.6 s, SSE = 10 ± 6.7 bpm and %OS = 3.2 ± 1.9%. Overall, both strategies produced a satisfying and reproducible performance, highlighting their potential use in persistent sinus tachycardia.

Corrections to "A Numerical Model of the Acute Cardiac Effects Provoked by Cervical Vagus Nerve Stimulation".

In the above paper [1] there are minor errors in several equations, which we correct here. There is also an error in Fig. 5, which we also correct here.

Electrocardiogram Delineation Using Deep Neural Networks.

BACKGROUND: In recent years, there has been a rising interest in the application of deep neural networks (DNN) for the delineation of the electrocardiogram (ECG). OBJECTIVES: A variety of DNN architectures has been investigated in a 5-fold cross-validation approach. RESULTS: The best performing network achieved 100% sensitivity and >97% positive predictive value for all ECG waves. CONCLUSION: Our DNN could achieve similar classification performance as other DNN approaches described in the literature at a reduced computational cost.

A Numerical Model of the Acute Cardiac Effects Provoked by Cervical Vagus Nerve Stimulation.

OBJECTIVE: Today, the diverse acute cardiac effects of vagus nerve stimulation (VNS) are still not fully understood. Therefore, we propose a numerical model that can predict the acute cardiac responses to VNS and explain the underlying mechanisms on different levels. METHODS: We integrated a model of vagal nerve fiber recruitment and acetylcholine (ACh) kinetics at vagal nerve terminals into a cardiovascular system model. A sensitivity analysis was performed to identify the most important parameters of vagal cardiac pathways. These parameters were tuned, and the model was validated based on published data of experiments in anesthetized sheep. RESULTS: The four most important parameters are related to vagus nerve anatomy (electrode-fiber distances, fiber diameters) and ACh kinetics in the vagal neuroeffector junction (rate of ACh release and -hydrolysis) which together explain >53% of the observed variability in acute cardiac responses to VNS. The mean electrode-fiber distance and nerve fiber diameters obtained from tuning are 1.3 ± 0.09 mm, and 4.9 ± 0.25 µm; the ACh release and -hydrolysis rate constants are 0.023 s-1 and 0.77 s-1, respectively. With this parameterization, the model could accurately predict published data on the acute cardiac effects of VNS. CONCLUSIONS: The model can explain the cardiac responses to VNS on multiple levels. The results highlight the importance of four parameters tied to ACh dynamics and vagus nerve anatomy for predicting the cardiac effects of VNS. SIGNIFICANCE: The model represents a substantial improvement in terms of comprehensibility of the underlying mechanisms of the acute cardiac responses to VNS.

Changes in Resting and Exercise Hemodynamics Early After Heart Transplantation: A Simulation Perspective.

Introduction: During heart transplantation (HTx), cardiac denervation is inevitable, thus typically resulting in chronic resting tachycardia and chronotropic incompetence with possible consequences in patient quality of life and clinical outcomes. To this date, knowledge of hemodynamic changes early after HTx is still incomplete. This study aims at providing a model-based description of the complex hemodynamic changes at rest and during exercise in HTx recipients (HTxRs). Materials and Methods: A numerical model of early HTxRs is developed that integrates intrinsic and autonomic heart rate (HR) control into a lumped-parameter cardiovascular system model. Intrinsic HR control is realized by a single-cell sinoatrial (SA) node model. Autonomic HR control is governed by aortic baroreflex and pulmonary stretch reflex and modulates SA node activity through neurotransmitter release. The model is tuned based on published clinical data of 15 studies. Simulations of rest and exercise are performed to study hemodynamic changes associated with HTxRs. Results: Simulations of HTxRs at rest predict a substantially increased HR [93.8 vs. 69.5 beats/min (bpm)] due to vagal denervation while maintaining normal cardiac output (CO) (5.2 vs. 5.6 L/min) through a reduction in stroke volume (SV) (55.4 vs. 82 mL). Simulations of exercise predict markedly reduced peak CO (13 vs. 19.8 L/min) primarily resulting from diminished peak HRs (133.9 vs. 169 bpm) and reduced ventricular contractility. Yet, the model results show that HTxRs can maintain normal CO for low- to medium-intensity exercise by increased SV augmentation through the Frank-Starling mechanism. Conclusion: Relevant hemodynamic changes occur after HTx. Simulations suggest that (1) increased resting HRs solely result from the absence of vagal tone; (2) chronotropic incompetence is the main limiting factor of exercise capacity whereby peripheral factors play a secondary role; and (3) despite the diminished exercise capacity, HTxRs can compensate chronotropic incompetence by a preload-mediated increase in SV augmentation and thus maintain normal CO in low- to medium-intensity exercise.