Enhancing Heart Failure Care: Deep Learning-Based Activity Classification in Left Ventricular Assist Device Patients.

Accurate activity classification is essential for the advancement of closed-loop control for left ventricular assist devices (LVADs), as it provides necessary feedback to adapt device operation to the patient's current state. Therefore, this study aims at using deep neural networks (DNNs) to precisely classify activity for these patients. Recordings from 13 LVAD patients were analyzed, including heart rate, LVAD flow, and accelerometer data, classifying activities into six states: active, inactive, lying, sitting, standing, and walking.

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.

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).

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.

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).

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.

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.

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.

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

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).