Robust correlation dimension estimator for heart rate variability

Introduction. Non-linear methods of analysis have found widespread use in the Heart Rate Variability (HRV) technology, when the long-term HRV records are available. Using one of the effective nonlinear methods of analysis of HRV correlation dimension D2 for the standard 5-min HRV records is suppressed by unsatisfactory accuracy of available methods in case of short records (usually, doctors have about 500 RRs during standard 5-min HRV record), as well as complexity and ambiguity of choosing additional parameters for known methods of calculating D2. The purpose of the work. Building a robust estimator for calculating correlation dimension D2 with high accuracy for limited se-ries of RR-intervals observed in a standard 5-minute HRV record, i. e. with N  500. As well as demonstrating the capabilities of the D2 formula on a well known attractors (Lorenz, Duffing, Hennon and etc.) and in applications for Normal Sinus Rhythm (NSR), Congestive Heart Failure (CHF) and Atrial Fibrillation (AF). Materials and Methods. We used MIT-BIH long-term HRV records for normal sinus rhythm, congestive heart failure and atrial fibrillation. In order to analyze the accuracy of new robust estimator for D2, we used the known theoretical values for some famous attractors (Lorenz, Duffing, Hennon and etc.) and the most popular Grassberger-Procaccia (G-P) algorithm for D2. The results of the study. We have shown the effectiveness of the developed D2 formula for time series of limited length (N = 500–1000) by some famous attractors (Lorenz, Duffing, Hennon and etc.) and with the most popular Grassberger-Procaccia (G-P) algorithm for D2. It was demonstrated statistically significant difference of D2 for normal sinus rhythm and congestive heart failure by standard 5 min HRV segments from MIT-BIH database. The promised technology for early prediction of atrial fibrillation episodes by current D2 algorithm was shown for standard 5 min HRV segments from MIT-BIH Atrial Fibrillation database. Conclusion. Robust correlation dimension D2 estimator suggested in the article allows for time series of limited length (N ≈ 500) to calculate D2 value that differs at mean from a precise one by 5 ± 4%, as demonstrated for various well known attractors (Lorenz, Duffing, Hennon and etc.). We have shown on the standard 5-min segments from MIT-BIH database of HRV records: - the statistically significant difference of D2 for cases of normal sinus rhythm and congestive heart failure; - D2 drop significantly for the about 30 min. before of AF and D2 growth drastically under AF there was shown for HRV records with Atrial Fibrillation (AF) episodes. The suggested robust correlation dimension D2 estimator is perfect suitable for real time HRV monitoring as accurate, fast and non-consuming for computing resources. Key words: Hearth rate variability; Correlation dimension; Congestive heart failure; Atrial fibrillation.

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

Volume 3: Biomedical and Biotechnology Engineering ◽

10.1115/imece2016-66836 ◽

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.

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ECG Classification using Machine Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d6989.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 2492-2494

Keyword(s):

Heart Failure ◽

Cardiovascular Disease ◽

Congestive Heart Failure ◽

Sinus Rhythm ◽

Normal Sinus Rhythm ◽

Original Data ◽

Ecg Signals ◽

Normal Sinus ◽

Electrocardiogram Signals

Recently, the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a important part in the clinical diagnosis of cardiovascular disease. Convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition to the input layer and the output layer, i.e., two convolution layers, two down sampling layers and one full connection layer, extracting the effective features from the original data and classifying the features using wavelet .The classification of ARR (Arrhythmia), CHF (Congestive Heart Failure), and NSR (Normal Sinus Rhythm) signals. The experimental results contains on ARR signals from the MIT-BIH arrhythmia,CHF signals from the BIDMC Congestive Heart Failure and NSR signals from the MIT-BIH Normal Sinus Rhythm Databases show that the proposed method achieves a promising classification accuracy of 90.63%, significantly outperforming several typical ECG classification methods.

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Bucindolol Decreases Atrial Fibrillation Burden in Patients with Heart Failure and the ADRB1 Arg389Arg Genotype

Circulation Arrhythmia and Electrophysiology ◽

10.1161/circep.120.009591 ◽

2021 ◽

Author(s):

Jonathan P. Piccini ◽

Christopher Dufton ◽

Ian A. Carroll ◽

Jeff S. Healey ◽

William T. Abraham ◽

...

Keyword(s):

Heart Failure ◽

Atrial Fibrillation ◽

Sinus Rhythm ◽

Normal Sinus Rhythm ◽

Left Ventricular ◽

Rhythm Control ◽

Left Ventricular Ejection ◽

Ventricular Ejection Fraction ◽

Normal Sinus

Background - Bucindolol is a genetically targeted β-blocker/mild vasodilator with the unique pharmacologic properties of sympatholysis and ADRB1 Arg389 receptor inverse agonism. In the GENETIC-AF trial conducted in a genetically defined heart failure (HF) population at high risk for recurrent atrial fibrillation (AF), similar results were observed for bucindolol and metoprolol succinate for the primary endpoint of time to first atrial fibrillation (AF) event; however, AF burden and other rhythm control measures were not analyzed. Methods - The prevalence of ECGs in normal sinus rhythm, AF interventions for rhythm control (cardioversion, ablation and antiarrhythmic drugs), and biomarkers were evaluated in the overall population entering efficacy follow-up (N=257). AF burden was evaluated for 24 weeks in the device substudy (N=67). Results - In 257 patients with HF the mean age was 65.6 ± 10.0 years, 18% were female, mean left ventricular ejection fraction (LVEF) was 36%, and 51% had persistent AF. Cumulative 24-week AF burden was 24.4% (95% CI: 18.5, 30.2) for bucindolol and 36.7% (95% CI: 30.0, 43.5) for metoprolol (33% reduction, p < 0.001). Daily AF burden at the end of follow-up was 15.1% (95% CI: 3.2, 27.0) for bucindolol and 34.7% (95% CI: 17.9, 51.2) for metoprolol (55% reduction, p < 0.001). For the metoprolol and bucindolol respective groups the prevalence of ECGs in normal sinus rhythm was 4.20 and 3.03 events per patient (39% increase in the bucindolol group, p < 0.001), while the rate of AF interventions was 0.56 and 0.82 events per patient (32% reduction for bucindolol, p = 0.011). Reductions in plasma norepinephrine (p = 0.038) and NT-proBNP (p = 0.009) were also observed with bucindolol compared to metoprolol. Conclusions - Compared with metoprolol, bucindolol reduced AF burden, improved maintenance of sinus rhythm, and lowered the need for additional rhythm control interventions in patients with HF and the ADRB1 Arg389Arg genotype.

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Similarities in Hospitalization Rates on Systolic Heart Failure Patients with Atrial Fibrillation Compared to Normal Sinus Rhythm

Journal of Cardiac Failure ◽

10.1016/j.cardfail.2005.06.329 ◽

2005 ◽

Vol 11 (6) ◽

pp. S177

Keyword(s):

Heart Failure ◽

Atrial Fibrillation ◽

Sinus Rhythm ◽

Normal Sinus Rhythm ◽

Systolic Heart Failure ◽

Heart Failure Patients ◽

Normal Sinus ◽

Hospitalization Rates

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Classification of Paroxysmal Atrial Fibrillation using Second Order System

Jurnal Teknologi ◽

10.11113/jt.v67.2765 ◽

2014 ◽

Vol 67 (3) ◽

Cited By ~ 3

Author(s):

Nurul Ashikin Abdul-Kadir ◽

Norlaili Mat Safri ◽

Mohd Afzan Othman

Keyword(s):

Atrial Fibrillation ◽

Sinus Rhythm ◽

Normal Sinus Rhythm ◽

Atrial Fibrillation Patient ◽

Heart Rhythm ◽

Second Order ◽

Order System ◽

Normal Sinus ◽

Specificity And Sensitivity ◽

Significant Difference

In this paper, we monitored and analyzed the characteristics of atrial fibrillation in patient using second order approach. Atrial fibrillation is a type of atria arrhythmias, disturbing the normal heart rhythm between the atria and lower ventricles of the heart. Heart disease and hypertension increase risk of stroke from atrial fibrillation. This study used electrocardiogram (ECG) signals from Physiobank, namely MIT-BIH Atrial Fibrillation Dataset and MIT-BIH Normal Sinus Rhythm Dataset. In total, 865 episodes for each type of ECG signal were classified, specifically normal sinus rhythm (NSR) of human without arrhythmia, normal sinus rhythm of atrial fibrillation patient (N) and atrial fibrillation (AF). Extracted parameters (forcing input, natural frequency and damping coefficient) from second order system were characterized and analyzed. Their ratios, time derivatives, and differential derivatives were also observed. Altogether, 12 parameters were extracted and analysed from the approach. The results show significant difference between the three ECGs of forcing input, and derivative of forcing input. Overall system performance gives specificity and sensitivity of 84.9 % and 85.5 %, respectively.

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Classification of human electrocardiograms by multi-layer convolutional neural network and hyperparameter optimization

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189610 ◽

2021 ◽

pp. 1-9

Author(s):

Yao-Mei Chen ◽

Yenming J. Chen ◽

Yun-Kai Tsai ◽

Wen-Hsien Ho ◽

Jinn-Tsong Tsai

Keyword(s):

Neural Network ◽

Heart Failure ◽

Congestive Heart Failure ◽

Sinus Rhythm ◽

Convolutional Neural Network ◽

Large Scale ◽

Normal Sinus Rhythm ◽

Performance Tests ◽

Hyperparameter Optimization ◽

Normal Sinus

A multi-layer convolutional neural network (MCNN) with hyperparameter optimization (HyperMCNN) is proposed for classifying human electrocardiograms (ECGs). For performance tests of the HyperMCNN, ECG recordings for patients with cardiac arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR) were obtained from three PhysioNet databases: MIT-BIH Arrhythmia Database, BIDMC Congestive Heart Failure Database, and MIT-BIH Normal Sinus Rhythm Database, respectively. The MCNN hyperparameters in convolutional layers included number of filters, filter size, padding, and filter stride. The hyperparameters in max-pooling layers were pooling size and pooling stride. Gradient method was also a hyperparameter used to train the MCNN model. Uniform experimental design approach was used to optimize the hyperparameter combination for the MCNN. In performance tests, the resulting 16-layer CNN with an appropriate hyperparameter combination (16-layer HyperMCNN) was used to distinguish among ARR, CHF, and NSR. The experimental results showed that the average correct rate and standard deviation obtained by the 16-layer HyperMCNN were superior to those obtained by a 16-layer CNN with a hyperparameter combination given by Matlab examples. Furthermore, in terms of performance in distinguishing among ARR, CHF, and NSR, the 16-layer HyperMCNN was superior to the 25-layer AlexNet, which was the neural network that had the best image identification performance in the ImageNet Large Scale Visual Recognition Challenge in 2012.

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