5,134 research outputs found
A Review of Atrial Fibrillation Detection Methods as a Service
Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals
Automatic Mode Switching in Atrial Fibrillation
Automatic mode switching (AMS) algorithms were designed to prevent tracking of atrial tachyarrhythmias (ATA) or other rapidly occurring signals sensed by atrial channels, thereby reducing the adverse hemodynamic and symptomatic consequences of a rapid ventricular response. The inclusion of an AMS function in most dual chamber pacemaker now provides optimal management of atrial arrhythmias and allows the benefit of atrioventricular synchrony to be extended to a population with existing atrial fibrillation. Appropriate AMS depends on several parameters: a) the programmed parameters; b) the characteristics of the arrhythmia; c) the characteristics of the AMS algorithm. Three qualifying aspects constitute an AMS algorithm: onset, AMS response, and resynchronization. Since AMS programs also provide data on the time of onset and duration of AMS episodes, AMS data may be interpreted as a surrogate marker of ATAs recurrence. Recently, stored electrograms corresponding to episodes of ATAs have been introduced, thus clarifying the accuracy of AMS in detecting ATAs Clinically this information may be used to assess the efficacy of an antiarrhythmic intervention or the risk of thromboembolic events, and it may serve as a valuable research tool for evaluating the natural history and burden of ATAs
Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine
Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76
Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs
We tackle the problem of classifying Electrocardiography (ECG) signals with
the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial
fibrillation is the most common type of arrhythmia, but in many cases PAF
episodes are asymptomatic. Therefore, in order to help diagnosing PAF, it is
important to design procedures for detecting and, more importantly, predicting
PAF episodes. We propose a method for predicting PAF events whose first step
consists of a feature extraction procedure that represents each ECG as a
multi-variate time series. Successively, we design a classification framework
based on kernel similarities for multi-variate time series, capable of handling
missing data. We consider different approaches to perform classification in the
original space of the multi-variate time series and in an embedding space,
defined by the kernel similarity measure. We achieve a classification accuracy
comparable with state of the art methods, with the additional advantage of
detecting the PAF onset up to 15 minutes in advance
Influence of autonomic nervous system in the inducibility of atrial fibrillation.
CĂlem tĂ©to práce je zjištÄ›nĂ zmÄ›n pĹ™edcházejĂcĂm fibrilaci sĂnĂ. Pozorována je rovnováha mezi sympatikem a parasympatikem. Do experimentu vĂ˝zkumnĂ©ho Ăşstavu CleavlendskĂ© kliniky bylo zapojeno šest psĹŻ rĹŻznĂ˝ch ras. Signály EKG byly zĂskány HolterovskĂ˝m 24hodinovĂ˝m monitorovánĂm. PomocĂ 40 vysokofrekvenÄŤnĂch impulsĹŻ (TI) byla kaĹľdĂ˝ch 30 minut vyvolávána AF. Z 24hodinovĂ©ho signálu byly extrahovány kratšà epizody. KaĹľdá z tÄ›chto epizod obsahovala 10 minut pĹ™edcházejĂcĂch TI a 3 minuty následujĂcĂ po TI. DesetiminutovĂ© epizody byly zpracovány automaticky, byly detekovány QRS komplexy a RR intervaly a vypoÄŤteny HRV parametry. PĹ™Ătomnost a dĂ©lka trvánĂ AF byly zjištÄ›ny manuálnÄ› z tĹ™ĂminutovĂ˝ch intervalĹŻ následujĂcĂch po TI. Byla-li vyvolána AF o dĂ©lce trvánĂ kratšà neĹľ 30 sekund došlo ve srovnánĂ s epizodami bez vĂ˝skytu AF k vĂ˝znamnĂ˝m zmÄ›nám třà HRV parametrĹŻ. HF parametr poklesl pro epizody s vĂ˝skytem AF. LF parametr byl naopak vyššà v epizodách s AF. Pro AF delšà neĹľ 30 sekund nebyly vĂ˝znamnĂ© zmÄ›ny pozorovány. ZmÄ›ny v epizodách s krátkou AF mohly bĂ˝t zpĹŻsobeny zmÄ›nami vlivu sympatiku a parasympatiku. Ke vzniku dlouhĂ˝ch AF je pravdÄ›podobnÄ› zapotĹ™ebĂ i jinĂ©ho vlivu, kterĂ˝ nemusĂ nutnÄ› souviset s nervovĂ˝m systĂ©mem. K dalšĂm analĂ˝zám je zapotĹ™ebĂ vÄ›tšĂho mnoĹľstvĂ signálĹŻ.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.
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