The frequency of atrial fibrillatory waves is modulated by the spatiotemporal pattern of acetylcholine release: a 3D computational study.

In atrial fibrillation (AF), the ECG P-wave, which represents atrial depolarization, is replaced with chaotic and irregular fibrillation waves (f waves). The f-wave frequency, , shows significant variations over time. Cardiorespiratory interactions regulated by the autonomic nervous system have been suggested to play a role in such variations.

Intensive care photoplethysmogram datasets and machine-learning for blood pressure estimation: Generalization not guarantied.

The large MIMIC waveform dataset, sourced from intensive care units, has been used extensively for the development of Photoplethysmography (PPG) based blood pressure (BP) estimation algorithms. Yet, because the data comes from patients in severe conditions-often under the effect of drugs-it is regularly noted that the relationship between BP and PPG signal characteristics may be anomalous, a claim that we investigate here. A sample of 12,000 records from the MIMIC waveform dataset was stacked up against the 219 records of the PPG-BP dataset, an alternative public dataset obtained under controlled experimental conditions.

Heart rate characteristic based modelling of atrial fibrillatory rate using implanted cardiac monitor data.

. The objective of the present study is to investigate the feasibility of using heart rate characteristics to estimate atrial fibrillatory rate (AFR) in a cohort of atrial fibrillation (AF) patients continuously monitored with an implantable cardiac monitor. We will use a mixed model approach to investigate population effect and patient specific effects of heart rate characteristics on AFR, and will correct for the effect of previous ablations, episode duration, and onset date and time.

A subspace projection approach to quantify respiratory variations in the f-wave frequency trend.

The autonomic nervous system (ANS) is known as a potent modulator of the initiation and perpetuation of atrial fibrillation (AF), hence information about ANS activity during AF may improve treatment strategy. Respiratory induced ANS variation in the f-waves of the ECG may provide such information. This paper proposes a novel approach for improved estimation of such respiratory induced variations and investigates the impact of deep breathing on the f-wave frequency in AF patients.

An atrioventricular node model incorporating autonomic tone.

The response to atrial fibrillation (AF) treatment is differing widely among patients, and a better understanding of the factors that contribute to these differences is needed. One important factor may be differences in the autonomic nervous system (ANS) activity. The atrioventricular (AV) node plays an important role during AF in modulating heart rate.

Acute Cardiovascular Effects of Hydrotreated Vegetable Oil Exhaust.

Ambient air pollution is recognized as a key risk factor for cardiovascular morbidity and mortality contributing to the global disease burden. The use of renewable diesel fuels, such as hydrotreated vegetable oil (HVO), have increased in recent years and its impact on human health are not completely known. The present study investigated changes in cardiovascular tone in response to exposure to diluted HVO exhaust.

Non-invasive Characterization of Human AV-Nodal Conduction Delay and Refractory Period During Atrial Fibrillation.

During atrial fibrillation (AF), the heart relies heavily on the atrio-ventricular (AV) node to regulate the heart rate. Thus, characterization of AV-nodal properties may provide valuable information for patient monitoring and prediction of rate control drug effects. In this work we present a network model consisting of the AV node, the bundle of His, and the Purkinje fibers, together with an associated workflow, for robust estimation of the model parameters from ECG.

Detection of Brief Episodes of Atrial Fibrillation Based on Electrocardiomatrix and Convolutional Neural Network.

Brief episodes of atrial fibrillation (AF) may evolve into longer AF episodes increasing the chances of thrombus formation, stroke, and death. Classical methods for AF detection investigate rhythm irregularity or P-wave absence in the ECG, while deep learning approaches profit from the availability of annotated ECG databases to learn discriminatory features linked to different diagnosis. However, some deep learning approaches do not provide analysis of the features used for classification.

Respiratory Induced Modulation in f-Wave Characteristics During Atrial Fibrillation.

The autonomic nervous system (ANS) is an important factor in cardiac arrhythmia, and information about ANS activity during atrial fibrillation (AF) may contribute to personalized treatment. In this study we aim to quantify respiratory modulation in the f-wave frequency trend from resting ECG. First, an f-wave signal is extracted from the ECG by QRST cancelation.

Characterization of Changes in P-Wave VCG Loops Following Pulmonary-Vein Isolation.

Atrial fibrillation is the most common type of cardiac arrhythmia in clinical practice. Currently, catheter ablation for pulmonary-vein isolation is a well-established treatment for maintaining sinus rhythm when antiarrhythmic drugs do not succeed. Unfortunately, arrhythmia recurrence after catheter ablation remains common, with estimated rates of up to 45%.

Detection of Needle Dislodgement Using Extracorporeal Pressure Signals: A Feasibility Study.

Venous needle dislodgement (VND) during dialysis is a rarely occurring adverse event, which becomes life-threatening if not handled promptly. Because the standard venous pressure alarm, implemented in most dialysis machines, has low sensitivity, a novel approach using extracted cardiac information to detect needle dislodgement is proposed. Four features are extracted from the arterial and venous pressure signals of the dialysis machine, characterizing the mean venous pressure, the venous cardiac pulse pressure, the time delay, and the correlation between the two pressure signals.

ECG-Derived Respiratory Rate in Atrial Fibrillation.

Objective: The present study addresses the problem of estimating the respiratory rate from the morphological ECG variations in the presence of atrial fibrillatory waves (f-waves). The significance of performing f-wave suppression before respiratory rate estimation is investigated.

Methods: The performance of a novel approach to ECG-derived respiration, named "slope range" (SR) and designed particularly for operation in atrial fibrillation (AF), is compared to that of two well-known methods based on either R-wave angle (RA) or QRS loop rotation angle (LA).

Monitoring respiration using the pressure sensors in a dialysis machine.

Objective: Although respiratory problems are common among patients with end-stage renal disease, respiration is not continuously monitored during dialysis. The purpose of the present study is to investigate the feasibility of monitoring respiration using the pressure sensors of the dialysis machine.

Approach: Respiration induces variations in the blood pressure that propagates to the extracorporeal circuit of the dialysis machine.

Changes in f-wave characteristics during cryoballoon catheter ablation.

Objective: Changes in ECG-derived parameters are studied in atrial fibrillation (AF) patients undergoing cryoballoon catheter ablation.

Approach: Parameters characterizing f-wave frequency, morphology by phase dispersion, and amplitude are estimated using a model-based statistical approach. These parameters are studied before, during, and after ablation, as well as for AF type (paroxysmal/persistent).

Model-Based Assessment of f-Wave Signal Quality in Patients With Atrial Fibrillation.

Objective: The detection and analysis of atrial fibrillation (AF) in the ECG is greatly influenced by signal quality. The present study proposes and evaluates a model-based f-wave signal quality index (SQI), denoted , for use in the QRST-cancelled ECG signal.

Methods: is computed using a harmonic f-wave model, allowing for variation in frequency and amplitude.

Detection of ventricular premature beats based on the pressure signals of a hemodialysis machine.

Monitoring of ventricular premature beats (VPBs), being abundant in hemodialysis patients, can provide information on cardiovascular instability and electrolyte imbalance. In this paper, we describe a method for VPB detection which explores the signals acquired from the arterial and the venous pressure sensors, located in the extracorporeal blood circuit of a hemodialysis machine. The pressure signals are mainly composed of a pump component and a cardiac component.

Characterisation of human AV-nodal properties using a network model.

Characterisation of the AV-node is an important step in determining the optimal form of treatment for supraventricular tachycardias. To integrate and analyse patient-specific measurements, mathematical modelling has emerged as a valuable tool. Here we present a model of the human AV-node, consisting of a series of interacting nodes, each with separate dynamics in refractory time and conduction delay.

Clinical Use And Limitations Of Non-Invasive Electrophysiological Tests In Patients With Atrial Fibrillation.

Atrial fibrillation (AF) is a complex arrhythmia, that has been studied non-invasively assessing atrial refractory period, atrioventricular node (AV) node refractory period, and ventricular response. The AV node plays a fundamental role as it filters many of the numerous irregular atrial impulses bombarding the node. Despite its importance, the electrophysiological (EP) characteristics of the AV node are not routinely evaluated since conventional EP techniques for assessment of refractory period or conduction velocity of the AV node are not applicable in AF.

A Statistical Atrioventricular Node Model Accounting for Pathway Switching During Atrial Fibrillation.

Objective: The atrioventricular (AV) node plays a central role in atrial fibrillation (AF), as it influences the conduction of impulses from the atria into the ventricles. In this paper, the statistical dual pathway AV node model, previously introduced by us, is modified so that it accounts for atrial impulse pathway switching even if the preceding impulse did not cause a ventricular activation.

Methods: The proposed change in model structure implies that the number of model parameters subjected to maximum likelihood estimation is reduced from five to four.

Noninvasive characterization of atrioventricular conduction in patients with atrial fibrillation.

The atrioventricular (AV) node plays a fundamental role in patients with atrial fibrillation (AF), acting as a filter to the numerous irregular atrial impulses which bombard the node. A phenomenological approach to better understand AV nodal electrophysiology is to analyze the ventricular response with respect to irregularity. In different cohorts of AF patients, such analysis has been performed with the aim to evaluate the association between ventricular response characteristics and long-term clinical outcome and to determine whether irregularity is affected by rate-control drugs.

Non-invasive assessment of the effect of beta blockers and calcium channel blockers on the AV node during permanent atrial fibrillation.

Aim: We aimed at assessing changes in AV nodal properties during administration of the beta blockers metoprolol and carvedilol, and the calcium channel blockers diltiazem and verapamil from electrocardiographic data.

Methods: Parameters accounting for the functional refractory periods of the slow and fast pathways (aRPs and aRPf) were estimated using atrial fibrillatory rate (AFR) and ventricular response assessed from 15-min ECG segments recorded at baseline and on drug treatment from sixty patients with permanent AF.

Results: The results showed that AFR and HR were significantly reduced for all drugs, and that aRPs and aRPf were significantly prolonged in both pathways.

Extracting a cardiac signal from the extracorporeal pressure sensors of a hemodialysis machine.

Although patients undergoing hemodialysis treatment often suffer from cardiovascular disease, monitoring of cardiac rhythm is not performed on a routine basis. Without requiring any extra sensor, this study proposes a method for extracting a cardiac signal from the built-in extracorporeal venous pressure sensor of the hemodialysis machine. The extraction is challenged by the fact that the cardiac component is much weaker than the pressure component caused by the peristaltic blood pump.