Influence of autonomic nervous system in the inducibility of atrial fibrillation.

Cílem této práce je zjištění změn předcházejícím fibrilaci síní. Pozorována je rovnováha mezi sympatikem a parasympatikem. Do experimentu výzkumného ústavu Cleavlendské kliniky bylo zapojeno šest psů různých ras. Signály EKG byly získány Holterovským 24hodinovým monitorováním. Pomocí 40 vysokofrekvenčních impulsů (TI) byla každých 30 minut vyvolávána AF. Z 24hodinového signálu byly extrahovány kratší epizody. Každá z těchto epizod obsahovala 10 minut předcházejících TI a 3 minuty následující po TI. Desetiminutové epizody byly zpracovány automaticky, byly detekovány QRS komplexy a RR intervaly a vypočteny HRV parametry. Přítomnost a délka trvání AF byly zjištěny manuálně z tříminutových intervalů následujících po TI. Byla-li vyvolána AF o délce trvání kratší než 30 sekund došlo ve srovnání s epizodami bez výskytu AF k významným změnám tří HRV parametrů. HF parametr poklesl pro epizody s výskytem AF. LF parametr byl naopak vyšší v epizodách s AF. Pro AF delší než 30 sekund nebyly významné změny pozorovány. Změny v epizodách s krátkou AF mohly být způsobeny změnami vlivu sympatiku a parasympatiku. Ke vzniku dlouhých AF je pravděpodobně zapotřebí i jiného vlivu, který nemusí nutně souviset s nervovým systémem. K dalším analýzám je zapotřebí většího množství signálů.The aim of this study is to investigate changes in sympatho-vagal balance before the initiation of AF. Six mongrel dogs from the Cleveland Clinic foundation were included in this study. ECG was recorded for 24 hours using telemetric Holter monitoring. AF was periodically induced every 30 min. by applying brief bursts of 40 high-frequency atrial train impulses (TI). From the 24 hours signals' traces shorter data episodes were extracted. Each episode consisted of 10 minutes preceding the atrial burst, and 3 minutes following the (TI). The 10 minutes episodes were processed automatically to determine the QRS complexes and RR intervals, and to calculate the HRV parameters. The presence and the duration of AF were determined by manual examination in each of the 3 minutes intervals following the delivery of TI. When the AF was generated, but episodes of AF were shorter than 30 seconds, three HRV parameters were significantly different than when AF was not generated. The HF component was lower in episodes that generated AF. The LF component was higher in episodes that generated AF. No significant differences were found when episodes of AF were longer than 30 seconds. Short episodes of AF could be generated when a certain disorder between sympathetic and parasympathetic tone is present. However in order to be able to generate longer AF episodes it is necessary another component not necessary related to the nervous system. Further analysis with a higher number of dogs should be needed.

Automated Atrial Fibrillation Detection by ECG Signal Processing: A Review

Cardiovascular diseases are the main cause of death in the world, according to the World Health Organization. Among them, ischemic heart disease is at the top, followed by a stroke. Several studies have revealed that atrial fibrillation (AF), which is the most common cardiac arrhythmia, increases up to five fold the overall risk of stroke. As AF can be asymptomatic, approximately 20% of the AF cases remain undiagnosed. AF can be detected by analyzing electrocardiography records. Many studies have been conducted to develop automatic methods for AF detection. This paper reviews some of the most relevant methods, classified into three groups: analysis of heart rate variability, analysis of the atrial activity, and hybrid methods. Their benefits and limitations are analyzed and compared, and our beliefs about where AF automatic detection research could be addressed are presented to improve its effectiveness and performance. © 2021 by Begell House, Inc

Estimating spectral HRV features with missing data

Abstract. Physiological signals, ECG signal, have been widely used for diagnosis, disease identification and nowadays for self-monitoring. Missing data represents the problem in spectral analysis. This study focuses on the HRV power spectral analysis in frequency-domain using three methods with simulated missing data in real RR interval tachograms. Actual missing ECG data are collected from the unconstrained measurement. Parametric, Non-parametric and uneven sampling approach were used for calculating the power spectral density (PSD), and cubic spline interpolation method was used for the non-parametric method. Based on this studies outcome, the effect of missing R-R interval data and optimal method was observed through the simulated real R-R interval tachograms for missing data. About 0 to 6 percentage data were removed according to the exponential Poisson distribution from the real R-R interval data for normal sinus rhythm, atrial fibrillation, tachycardia and bradycardia patient which data obtained from MIT-BIH Arrhythmia database to simulate real-world packet loss. For this analysis, 5 min duration data were used in all and 1000 Monte Carlo runs is performed for certain percentage missing data. Power spectral density (PSD) corresponding each frequency component was estimated as the frequency-domain parameters in each run and error power percentage based on each element difference between with and without the missing data duration were calculated. In addition, this study revealed that power spectral entropy measurement from power spectral density which differentiates between different arrhythmias

Rapidly detecting disorder in rhythmic biological signals: A spectral entropy measure to identify cardiac arrhythmias

We consider the use of a running measure of power spectrum disorder to distinguish between the normal sinus rhythm of the heart and two forms of cardiac arrhythmia: atrial fibrillation and atrial flutter. This spectral entropy measure is motivated by characteristic differences in the spectra of beat timings during the three rhythms. We plot patient data derived from ten-beat windows on a "disorder map" and identify rhythm-defining ranges in the level and variance of spectral entropy values. Employing the spectral entropy within an automatic arrhythmia detection algorithm enables the classification of periods of atrial fibrillation from the time series of patients' beats. When the algorithm is set to identify abnormal rhythms within 6 s it agrees with 85.7% of the annotations of professional rhythm assessors; for a response time of 30 s this becomes 89.5%, and with 60 s it is 90.3%. The algorithm provides a rapid way to detect atrial fibrillation, demonstrating usable response times as low as 6 s. Measures of disorder in the frequency domain have practical significance in a range of biological signals: the techniques described in this paper have potential application for the rapid identification of disorder in other rhythmic signals.Comment: 11 page

Photoplethysmography based atrial fibrillation detection: an updated review from July 2019

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field. This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 59 pertinent studies. Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis

Circadian Rhythms of Atrioventricular Conduction Properties in Chronic Atrial Fibrillation With and Without Heart Failure

AbstractObjectives. We examined the circadian variations in atrioventricular (AV) conduction properties during atrial fibrillation (AF) by a technique based on the Lorenz plot of successive ventricular response (VR) intervals and analyzed their relations with clinical features.Background. The VR interval in chronic AF shows circadian variation, which is attenuated in patients with an increased risk of death. Although the VR interval is determined by the dynamic processes in the AV node randomly stimulated by rapid atrial activity, the circadian variations of the AV conduction properties related to this mechanism are unknown.Methods. In 48 patients with chronic AF, Lorenz plots were generated on overlapping sequential segments of 512 VR intervals in 24-h ambulatory electrocardiograms. For each scatter plot, the 1.0-s intercept of the lower envelope (LE1.0) of the plot and the degree of scatter above the envelope (root mean square difference from the envelope [scattering index]) were measured for estimating AV node refractoriness and concealed AV conduction, respectively.Results. In all patients, a significant circadian rhythm was observed for the average VR interval, LE1.0and scattering index, with an acrophase occurring at night. The mesor, amplitude and acrophase of LE1.0and the scattering index closely and independently correlated with the corresponding rhythm variables of the average VR interval (partial r20.98, 0.86 and 0.68 for LE1.0and 0.98, 0.92 and 0.92 for scattering index). The amplitudes of these measures were lower in patients with congestive heart failure (CHF) even after adjustment for the effects of age, duration of AF, medications, left atrial diameter and blood pressure (p < 0.01 for all).Conclusions. These results suggest that 1) both AV node refractoriness and the degree of concealed AV conduction during AF may show a circadian rhythm; 2) the circadian rhythms of these properties may independently contribute to the circadian variation of the VR interval; and 3) these circadian rhythms may be attenuated in patients with CHF

Role of Editing of R–R Intervals in the Analysis of Heart Rate Variability

This paper reviews the methods used for editing of the R–R interval time series and how this editing can influence the results of heart rate (HR) variability analyses. Measurement of HR variability from short and long-term electrocardiographic (ECG) recordings is a non-invasive method for evaluating cardiac autonomic regulation. HR variability provides information about the sympathetic-parasympathetic autonomic balance. One important clinical application is the measurement of HR variability in patients suffering from acute myocardial infarction. However, HR variability signals extracted from R–R interval time series from ambulatory ECG recordings often contain different amounts of artifact. These false beats can be either of physiological or technical origin. For instance, technical artifact may result from poorly fastened electrodes or be due to motion of the subject. Ectopic beats and atrial fibrillation are examples of physiological artifact. Since ectopic and other false beats are common in the R–R interval time series, they complicate the reliable analysis of HR variability sometimes making it impossible. In conjunction with the increased usage of HR variability analyses, several studies have confirmed the need for different approaches for handling false beats present in the R–R interval time series. The editing process for the R–R interval time series has become an integral part of these analyses. However, the published literature does not contain detailed reviews of editing methods and their impact on HR variability analyses. Several different editing and HR variability signal pre-processing methods have been introduced and tested for the artifact correction. There are several approaches available, i.e., use of methods involving deletion, interpolation or filtering systems. However, these editing methods can have different effects on HR variability measures. The effects of editing are dependent on the study setting, editing method, parameters used to assess HR variability, type of study population, and the length of R–R interval time series. The purpose of this paper is to summarize these pre-processing methods for HR variability signal, focusing especially on the editing of the R–R interval time series