The Role of Heart Rate Variability in Atrial ECG Components of Normal Sinus Rhythm and Sinus Tachycardia Subjects

2020 ◽  
pp. 637-644
Author(s):  
B. Dhananjay ◽  
J. Sivaraman
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sinus Rhythm ◽  
Sinus Tachycardia ◽  
Normal Sinus Rhythm ◽  
Normal Sinus

Nonlinear Characterization of Heart Rate Variability in Normal Sinus Rhythm, Atrial Fibrillation and Congestive Heart Failure

2016 ◽  
Author(s):  
Syed Hassan Zaidi ◽  
Imran Akhtar ◽  
Syed Imran Majeed ◽  
Tahir Zaidi ◽  
Muhammad Saif Ullah Khalid
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Sinus Rhythm ◽  
Time Series Data ◽  
Normal Sinus Rhythm ◽  
Series Data ◽  
Normal Sinus

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.

scholarly journals Impact of heart rate variability in patients with normal sinus rhythm on image quality in coronary magnetic angiography

2008 ◽  
Vol 28 (1) ◽  
pp. 74-79 ◽  
Author(s):  
Tarinee Tangcharoen ◽  
Cosima Jahnke ◽  
Uwe Koehler ◽  
Bernhard Schnackenburg ◽  
Christoph Klein ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Image Quality ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Normal Sinus

Analysis of autonomic nervous pattern in hypertension based on short-term heart rate variability

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ruiqi Zhang ◽  
Zhengchun Hua ◽  
Chen Chen ◽  
Guangyuan Liu ◽  
Wanhui Wen
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Low Frequency ◽  
Support Vector ◽  
Short Term ◽  
Hypertensive Patients ◽  
Autonomic Nervous ◽  
Normal Sinus

AbstractPhysiological studies have found that the autonomic nervous system plays an important role in controlling blood pressure values. This paper, based on machine learning approaches, analysed short-term heart rate variability to determine differences in autonomic nervous function between hypertensive patients and normal population. The electrocardiogram (ECG) of hypertensive patients are 137 ECG recordings provided by Smart Health for Assessing the Risk of Events via ECG (SHAREE database). The RR intervals of healthy subjects include the data of 18 subjects from the MIT-BIH Normal Sinus Rhythm Database (nsrdb) and 54 subjects from the Normal Sinus Rhythm RR Interval Database (nsr2db). In this paper, each RR segment includes continuous 500 beats. Seventeen features were extracted to distinguish the hypertensive heart beat rhythms from the normal ones, and Kolmogorov-Smirnov test and sequential backward selection (SBS) were applied to get the best feature combinations. In addition, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) were applied as classifiers in the study. The performance of each classifier was evaluated independently using the leave-one-subject-out validation method. The best predictive model was based on RF and enabled to identify hypertensive patients by five features with an accuracy of 86.44%. The best five HRV features are sample entropy (SampEn), very low frequency spectral powers (VLF), root mean square of successful differences (RMSSD), ratio of low frequency spectral powers and high frequency spectral powers (LF/HF) and vector angle index (VAI). The results of the study show sympathetic overactivity and decreased parasympathetic tone in hypertensive patients.

scholarly journals Detection of coronary artery disease by reduced features and extreme learning machine

2018 ◽  
Vol 91 (2) ◽  
pp. 166-175 ◽  
Author(s):  
Ram Sewak Singh ◽  
Barjinder Singh Saini ◽  
Ramesh Kumar Sunkaria
Keyword(s):  
Coronary Artery Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Coronary Artery ◽  
Discriminant Analysis ◽  
Normal Sinus Rhythm ◽  
Normal Sinus ◽  
Hidden Node ◽  
Artery Disease ◽  
Learning Machine

Objective. Cardiovascular diseases generate the highest mortality in the globe population, mainly due to coronary artery disease (CAD) like arrhythmia, myocardial infarction and heart failure. Therefore, an early identification of CAD and diagnosis is essential. For this, we have proposed a new approach to detect the CAD patients using heart rate variability (HRV) signals. This approach is based on subspaces decomposition of HRV signals using multiscale wavelet packet (MSWP) transform and entropy features extracted from decomposed HRV signals. The detection performance was analyzed using Fisher ranking method, generalized discriminant analysis (GDA) and binary classifier as extreme learning machine (ELM). The ranking strategies designate rank to the available features extracted by entropy methods from decomposed heart rate variability (HRV) signals and organize them according to their clinical importance. The GDA diminishes the dimension of ranked features. In addition, it can enhance the classification accuracy by picking the best discerning of ranked features. The main advantage of ELM is that the hidden layer does not require tuning and it also has a fast rate of detection.Methodology. For the detection of CAD patients, the HRV data of healthy normal sinus rhythm (NSR) and CAD patients were obtained from  a standard database. Self recorded data as normal sinus rhythm (Self_NSR) of healthy subjects were also used in this work. Initially, the HRV time-series was decomposed to 4 levels using MSWP transform. Sixty two features were extracted from decomposed HRV signals by non-linear methods for HRV analysis, fuzzy entropy (FZE) and Kraskov nearest neighbour entropy (K-NNE). Out of sixty-two features, 31 entropy features were extracted by FZE and 31 entropy features were extracted by K-NNE method. These features were selected since every feature has a different physical premise and in this manner concentrates and uses HRV signals information in an assorted technique. Out of 62 features, top ten features were selected, ranked by a ranking method called as Fisher score. The top ten features were applied to the proposed model, GDA with Gaussian or RBF kernal + ELM having hidden node as sigmoid or multiquadric. The GDA method transforms top ten features to only one feature and ELM has been used for classification.Results. Numerical experimentations were performed on the combination of datasets as NSR-CAD and Self_NSR- CAD subjects. The proposed approach has shown better performance using top ten ranked entropy features. The GDA with RBF kernel + ELM having hidden node as multiquadric method and GDA with Gaussian kernel + ELM having hidden node as sigmoid or multiquadric method achieved an approximate detection accuracy of 100% compared to ELM and linear discriminant analysis (LDA)+ELM for both datasets. The subspaces level-4 and level-3 decomposition of HRV signals by MSWP transform can be used for detection and analysis of CAD patients.

scholarly journals Electrocardiographic indices of clinically healthy Chios sheep

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Aliasghar Chalmeh ◽  
Iman Saadat Akhtar ◽  
Mohamad Hadi Zarei ◽  
Mehrdad Badkoubeh
Keyword(s):  
Sinus Rhythm ◽  
Sinus Tachycardia ◽  
Normal Sinus Rhythm ◽  
Sinus Arrhythmia ◽  
Lead System ◽  
Normal Sinus ◽  
Qrs Duration ◽  
Electrocardiographic Findings ◽  
Chios Sheep ◽  
Electrical Activities

Information regarding normal electrocardiographic features of different breeds of animals can help veterinarians to detect any abnormalities in cardiac electrical activities. The current research was conducted to present the normal electrocardiographic indices of clinically healthy Chios ewes and lambs. The electrocardiograms were recorded from clinically healthy Chios ewes (n=27; 2-3 years old) and lambs (n=20; 4-6 months old) by using base apex lead system. T and QRS-duration in lambs were significantly lower than adult Chios ewes. The electrocardiographic amplitudes in lambs were lower than ewes, non-significantly. P-R, R-R, Q-T and S-T intervals in Chios lambs were significantly lower than ewes. No normal sinus rhythm was detected in Chios lambs. The proportion of sinus arrhythmia and sinus tachycardia in lambs was significantly more than ewes. Sino-atrial block was also detected in lambs. Based on the presented data it could be stated that aging can affect electrocardiographic findings of Chios sheep. Finally, our results will provide a good basis for judging the electrocardiograms in base apex lead system of Chios lambs and ewes.

Hypercoagulability Promotes Atrial Fibrosis and Fibrillation

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 4246-4246
Author(s):  
Henri M.H. Spronk ◽  
Anne-Margreet de Jong ◽  
Hetty C. de Boer ◽  
Alexander Maas ◽  
Sander Verheule ◽  
...  
Keyword(s):  
Sinus Rhythm ◽  
Cardiac Fibroblasts ◽  
Normal Sinus Rhythm ◽  
Factor Xa ◽  
P Wave ◽  
Wild Type ◽  
Normal Sinus ◽  
Episode Duration ◽  
Surface Electrocardiogram

Abstract Background: It is well known that atrial fibrillation (AF) induces a hypercoagulable state, which significantly increases stroke risk in patients with AF contributing to morbidity and mortality in these patients. Active coagulation factors can also provoke diverse cellular responses through stimulation of protease-activated receptors (PARs). In the heart and vessels, coagulation factor mediated PAR activation may provoke and mediate pro-inflammatory and tissue remodeling responses, potentially contributing to organ damage. We hypothesized that the onset and progression of AF, may be affected by hypercoagulability-mediated cell signaling responses, in the heart. Methods and results: To study the potential role of PARs in the structural remodeling process that renders the atria more prone to AF we first investigated whether thrombin or factor Xa could induce atrial fibroblast remodeling. In isolated rat cardiac fibroblasts, thrombin enhanced the phosphorylation of the pro-fibrotic signaling molecules Akt and Erk, and increased expression of TGFβ1 (2.7 fold) and the pro-inflammatory factor monocyte chemo-attractant protein-1 (6.1 fold). Thrombin also increased the incorporation of 3H-proline suggesting enhanced collagen synthesis by cardiac fibroblasts (2.5 fold). Differentiation towards myofibroblasts was indicated by increased expression of smooth muscle actin (2 fold). All effects could be prevented by the direct thrombin inhibitor dabigatran and comparable results were obtained for stimulation with factor Xa and inhibition with rivaroxaban, respectively. Next we studied whether enhanced stimulation of PARs
by chronic elevation of thrombin levels would lead to an enhanced vulnerability to AF in transgenic mice. In mice with enhanced thrombin activity due to a mutation in the thrombomodulin gene resulting in impaired thrombin inhibition (TMpro/pro), inducibility of AF episodes provoked by burst pacing was higher (6 out of 10 versus 1 out of 10 in wild type) and the duration of AF episodes was longer (episodes >2s in 6 out of 10 versus 0 out of 10 in wild type). Finally, we showed that inhibition of the coagulation cascade attenuated the development of AF in a goat model of AF. In 6 goats with persistent AF and treated with the anticoagulant nadroparine (4 weeks, 150 IU/kg twice daily) the complexity of the AF substrate was less pronounced compared to control animals. The conduction heterogeneity and block were 33% shorter in the nadroparine treated animals (maximal conduction time 23.3±3.1ms in control versus 15.7±2.1ms in nadroparine, p<0.05) and AF-induced a-SMA expression and endomysial fibrosis were less pronounced. Conclusion: The hypercoagulable state during AF provokes pro-fibrotic and pro-inflammatory responses in cardiac fibroblasts, as well as promotes the development of a substrate for AF in transgenic mice and in goats with persistent AF. Together, these results strongly support the role of hypercoagulability and PAR activation in the development of a substrate for AF. In addition, direct anticoagulant treatment may protect against AF-related cellular atrial remodelling. Figure 1: Enhanced AF inducibility and prolonged AF duration in TMpro/pro mice. Transesophageal stimulation was used to test AF inducibility. A surface electrocardiogram (lead I, sampled at 2.5 kHz) was recorded to detect AF. A) Traces show an example of a Wt mouse, returning to normal sinus rhythm immediately after the burst (upper panel) and a TMpro/pro mouse, showing a 3s episode of AF before returning to sinus rhythm (lower panel). In both cases, the first P wave observed after the burst is indicated. B) AF was inducible in 1 out of 10 Wt mice and 6 out of 10 TMpro/pro mice. C) Distribution of the longest AF episode duration observed in each Wt and TMpro/pro mouse. Figure 1:. Enhanced AF inducibility and prolonged AF duration in TMpro/pro mice. Transesophageal stimulation was used to test AF inducibility. A surface electrocardiogram (lead I, sampled at 2.5 kHz) was recorded to detect AF. A) Traces show an example of a Wt mouse, returning to normal sinus rhythm immediately after the burst (upper panel) and a TMpro/pro mouse, showing a 3s episode of AF before returning to sinus rhythm (lower panel). In both cases, the first P wave observed after the burst is indicated. B) AF was inducible in 1 out of 10 Wt mice and 6 out of 10 TMpro/pro mice. C) Distribution of the longest AF episode duration observed in each Wt and TMpro/pro mouse. Disclosures No relevant conflicts of interest to declare.

Amiodarone for the Maintenance of Sinus Rhythm in Patients with Atrial Fibrillation

1995 ◽  
Vol 29 (6) ◽  
pp. 596-602 ◽  
Author(s):  
Patricia A Howard
Keyword(s):  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Randomized Trials ◽  
Normal Sinus Rhythm ◽  
Efficacy And Safety ◽  
Long Term Effects ◽  
Normal Sinus ◽  
Review Articles

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.

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