Detection of Long Term Variations of Heart Rate Variability in Normal Sinus Rhythm and Atrial Fibrillation ECG Data

Author(s):  
Desok Kim ◽  
Yunhwan Seo ◽  
Woo Ram Jung ◽  
Chan-Hyun Youn
Author(s):  
Syed Hassan Zaidi ◽  
Imran Akhtar ◽  
Syed Imran Majeed ◽  
Tahir Zaidi ◽  
Muhammad Saif Ullah Khalid

This paper highlights the application of methods and techniques from nonlinear analysis to illustrate their far superior capability in revealing complex cardiac dynamics under various physiological and pathological states. The purpose is to augment conventional (time and frequency based) heart rate variability analysis, and to extract significant prognostic and clinically relevant information for risk stratification and improved diagnosis. In this work, several nonlinear indices are estimated for RR intervals based time series data acquired for Healthy Sinus Rhythm (HSR) and Congestive Heart Failure (CHF), as the two groups represent different cases of Normal Sinus Rhythm (NSR). In addition to this, nonlinear algorithms are also applied to investigate the internal dynamics of Atrial Fibrillation (AFib). Application of nonlinear tools in normal and diseased cardiovascular states manifest their strong ability to support clinical decision support systems and highlights the internal complex properties of physiological time series data such as complexity, irregularity, determinism and recurrence trends in cardiovascular regulation mechanisms.


1995 ◽  
Vol 29 (6) ◽  
pp. 596-602 ◽  
Author(s):  
Patricia A Howard

Objective: To discuss the role of amiodarone for the maintenance of normal sinus rhythm in patients with atrial fibrillation (AF) and review the clinical trial data evaluating the efficacy and safety of amiodarone in patients with AF. Data Sources: A MEDLINE search was used to identify pertinent literature. Additional references were identified from the articles obtained in the search. Key search terms were atrial fibrillation, amiodarone, and sinus rhythm. Study Selection: All studies available at the time the article was prepared evaluating the efficacy and safety of amiodarone in AF were included. In addition, review articles discussing the role of amiodarone in AF were selected. Data Extraction: NO large, prospective, randomized trials have been performed. Data from 8 nonrandomized and 2 randomized trials are reported. Information derived from review articles is discussed. Data Synthesis: In patients with AF, maintenance of normal sinus rhythm is desirable to eliminate symptoms, improve functional capacity, and reduce the risk of thromboembolic complications. Class IA agents traditionally have been used; however, concerns about long-term effects on mortality have focused attention on other agents such as amiodarone. A number of nonrandomized, uncontrolled trials have found amiodarone to be effective for maintaining normal sinus rhythm in patients with AF that is refractory to conventional agents. Two randomized, nonblind trials have found amiodarone's efficacy to be equal to or superior to that of class IA drugs. The findings of these trials must be weighed, however, against the significant potential for toxicity and drug interactions associated with amiodarone. Cardiovascular toxicities, including proarrhythmic effects, appear to be relatively rare. In contrast, noncardiovascular effects are common and potentially serious. Conclusions: Although the preliminary data using amiodarone in AF are encouraging, many questions remain unanswered. Prospective, randomized trials are needed to evaluate the long-term efficacy and safety of amiodarone in patients with AF. Studies also are needed to determine the optimal dosing regimen. Until these data are available, each patient must be evaluated individually, taking into account the relative benefits and risks of therapy. Amiodarone may be particularly useful in patients with significant risks for proarrhythmia and those whose AF is refractory to traditional therapy.


2008 ◽  
Vol 28 (1) ◽  
pp. 74-79 ◽  
Author(s):  
Tarinee Tangcharoen ◽  
Cosima Jahnke ◽  
Uwe Koehler ◽  
Bernhard Schnackenburg ◽  
Christoph Klein ◽  
...  

2015 ◽  
Vol 36 (9) ◽  
pp. 1873-1888 ◽  
Author(s):  
Marta Carrara ◽  
Luca Carozzi ◽  
Travis J Moss ◽  
Marco de Pasquale ◽  
Sergio Cerutti ◽  
...  

2017 ◽  
Vol 7 (3) ◽  
Author(s):  
Munish Sharma ◽  
Rohit Masih ◽  
Daniel A.N. Mascarenhas

Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide with an estimated number of 2.7-6.1 million cases in the United States (US) alone. The incidence of AF is expected to increase 2.5 fold over the next 50 years in the US. The management of AF is complex and includes mainly three aspects; restoration of sinus rhythm, control of ventricular rate and prevention of systemic thromboembolism. AF as a cause of systemic embolization has been well known for many years, and majority of patients are on oral anticoagulants (OACs) to prevent this. Many times, a patient may not be in AF chronically, nor is the AF burden (the amount of time patient is in AF out of the total monitored time) calculated. We present three cases of new onset transient AF triggered by temporary stressors. We were able to restore normal sinus rhythm (NSR) with chemical cardioversion. As per 2014 American College of Cardiology (ACC)/American Heart Association (AHA) recommendations, we started all three patients on OACs based on CHA<sub>2</sub>DS<sub>2</sub>VASc score <span style="text-decoration: underline;">&gt;</span>2. However, the patients refused long term OACs after restoration of NSR and correction of the temporary enticing stressors. In any case, the decision to start OACs would have had its own risks. Here we describe how antiarrhythmic drugs were used to maintain NSR, all while they were continuously monitored to determine the need to continue OACs.


Author(s):  
Shivaram Poigai Arunachalam ◽  
Elizabeth M. Annoni ◽  
Suraj Kapa ◽  
Siva K. Mulpuru ◽  
Paul A. Friedman ◽  
...  

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia affecting approximately 3 million Americans, and is a prognostic marker for stroke, heart failure and even death [1]. 12-lead electrocardiogram (ECG) is used to monitor normal sinus rhythm (NSR) and also detect AF. Although the persistent form of AF can be detected relatively easy, detecting paroxysmal AF is often a challenge since requiring continuous monitoring, which becomes expensive and cumbersome to collect lot of ECG data [1]. Several researchers have attempted to develop new methods to discriminate NSR and AF which are based on R-R interval analysis, linear methods, filtering, spectral analysis, statistical approaches such as entropy etc. which faces limitation of successfully detecting AF of all types with high sensitivity and specificity using short time ECG data [1–3]. The major issues with these approaches is that they often distort the ECG by several pre-processing steps with filters, do not provide reliable discrimination using short ECG time series data and many of them lack real-time capability that makes it difficult to trust the data for diagnosis and treatment. Both clinical and scientific communities recognize these difficulties and the necessity to develop novel methods that can enable accurate monitoring and detection of AF [2]. In addition, robust detection and classification algorithms are essential for delivering appropriate therapy for implantable cardioverter defibrillators (ICD) to provide lifesaving timely action. In this work, the authors propose and demonstrate the application of a multiscale frequency (MSF) approach [4] for accurate detection and discrimination between AF and NSR ECG traces taken from publically available Physionet database. The MSF approach takes into account the contribution from various frequencies in ECG and thus yield valuable information regarding the chaotic nature of AF. Therefore, we demonstrate that MSF can capture the complexity of AF which is associated with higher MSF value compared with NSR thus enabling robust discrimination e AF manifests itself with numerous chaotic frequencies within the body surface ECG,. We validate the feasibility of this technique to discriminate NSR from AF.


2021 ◽  
Vol 7 ◽  
pp. 205520762110196
Author(s):  
Christian Müller ◽  
Ulf Hengstmann ◽  
Michael Fuchs ◽  
Martin Kirchner ◽  
Frank Kleinjung ◽  
...  

Objective Early diagnosis of atrial fibrillation (AFib) is a priority for stroke prevention. We sought to test four commercial pulse detection systems (CPDSs) for ability to distinguish AFib from normal sinus rhythm using a published algorithm (Zhou et al., PLoS One 2015;10:e0136544), compared with visual diagnosis by electrocardiogram inspection. Methods BAYathlon was a prospective, non-interventional, single-centre study. Adult cardiology patients with documented AFib or sinus rhythm who were due to have a routine 5-min electrocardiogram were randomized to undergo a parallel 5-min pulse assessment with a Polar V800, eMotion Faros 360, TomTom heart rate monitor, or Adidas miCoach Smart Run. Results 144 patients (73 with AFib, 71 with sinus rhythm (based on electrocardiograms); median age: 73 years; 53.5% male) were analysed. Algorithm sensitivities (primary endpoint) and specificities for AFib when applied to CPDS recordings were 93.3% and 94.1% with the Polar V800, 90.0% and 84.2% with the eMotion Faros 360, and 0% and 100% with the other CPDSs (analysis period: 127 heart rate signals + 2 min). When applied to routine electrocardiograms, the algorithm correctly detected AFib in 71/73 patients. Different analysis periods (127 heart rate signals +1 or 3 min) only slightly changed the sensitivities with the Polar V800 and eMotion Faros 360 and had no effect on the sensitivities with the other CPDSs. Conclusion AFib screening using the applied algorithm is feasible with the Polar V800 and eMotion Faros 360 (which provide RR interval data) but not with the other CPDSs (which provide pre-processed heart rate time series). ClinicalTrials.gov identifier: NCT02875106


2004 ◽  
Vol 94 (12) ◽  
pp. 1563-1566 ◽  
Author(s):  
George E. Kochiadakis ◽  
Nikos E. Igoumenidis ◽  
Michail E. Hamilos ◽  
Panagiotis G. Tzerakis ◽  
Nikos C. Klapsinos ◽  
...  

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