Multi-scale transitions of fuzzy sample entropy of RR-intervals and their phase-randomized surrogates: A possibility to diagnose congestive heart failure

2017 ◽  
Vol 31 ◽  
pp. 350-356 ◽  
Author(s):  
Vinzenz von Tscharner ◽  
Payam Zandiyeh
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Sample Entropy ◽  
Rr Intervals ◽  
Multi Scale

scholarly journals Classification of Congestive Heart Failure from ECG Segments with a Multi-Scale Residual Network

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2019
Author(s):  
Dengao Li ◽  
Ye Tao ◽  
Jumin Zhao ◽  
Hang Wu
Keyword(s):  
Heart Failure ◽  
New York ◽  
Congestive Heart Failure ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Heart Association ◽  
Residual Network ◽  
Ecg Signals ◽  
Multi Scale ◽  
New York Heart Association

Congestive heart failure (CHF) poses a serious threat to human health. Once the diagnosis of CHF is established, clinical experts need to assess the severity of CHF in a timely manner. It is proved that electrocardiogram (ECG) signals are useful for assessing the severity of CHF. However, since the ECG perturbations are subtle, it is difficult for doctors to detect the differences of ECGs. In order to help doctors to make an accurate diagnosis, we proposed a novel multi-scale residual network (ResNet) to automatically classify CHF into four classifications according to the New York Heart Association (NYHA) functional classification system. Furthermore, in order to make the reported results more realistic, we used an inter-patient paradigm to divide the dataset, and segmented the ECG signals into two different intervals. The experimental results show that the proposed multi-scale ResNet-34 has achieved an average positive predictive value, sensitivity and accuracy of 93.49%, 93.44% and 93.60% respectively for two seconds of ECG segments. We have also obtained an average positive predictive value, sensitivity and accuracy of 94.16%, 93.79% and 94.29% respectively for five seconds of ECG segments. The proposed method can be used as an auxiliary tool to help doctors to classify CHF.

Abstract 2168: Efficient Detection of Atrial Fibrillation and Congestive Heart Failure in Single-Lead Devices

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Randall Moorman ◽  
Yuping Xiao ◽  
Douglas Lake
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Congestive Heart Failure ◽  
Primary Prevention ◽  
Rr Intervals ◽  
High Entropy ◽  
Efficient Detection ◽  
Entropy Measures ◽  
Ectopic Beats ◽  
Roc Area

Patients receiving primary prevention single lead ICDs are at risk for atrial fibrillation (AF) and congestive heart failure (CHF). No such device reports AF burden, and only a single CHF measure, trans-thoracic impedance, is available. Entropy measures that count the number of matching RR intervals have promise, as AF is random (high entropy) and CHF is often marked by reduced heart rate variability (RR intervals with many matches) and ectopic beats (few matches). We designed entropy-based measures to detect AF (high entropy) and CHF (mixture of RR intervals with many and with few matches). For real-world implementation, we used only 12 RR intervals, and calculated the result every 30 minutes in 24-hour Holter monitor records from the MIT-BIH databases. The Figure shows distinction among AF, NSR and CHF records using HR and S.D. (panel A) or the new entropy-based measures. Panel A shows poor diagnostic performance of conventional measures. In Panel B, the y-axis, COSEn, is the coefficient of sample entropy. The AF records all have higher values, and the ROC area is 1.00. The x-axis is a measure of template match counts. It distinguishes between normals and CHF patients with ROC area 0.92. With only 12 RR intervals every 30 minutes, entropy calculations allow for efficient detection of AF and CHF. We propose that single lead devices can be employed as monitors in the primary prevention population, where risk of AF and CHF is high.

scholarly journals A Classification System to Detect Congestive Heart Failure Using Second-Order Difference Plot of RR Intervals

2009 ◽  
Vol 2009 ◽  
pp. 1-7 ◽  
Author(s):  
R. A. Thuraisingham
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Success Rate ◽  
Classification System ◽  
Nearest Neighbor ◽  
Second Order ◽  
Rr Intervals ◽  
Order Difference ◽  
Holter Monitor ◽  
Difference Plot

A classification system to detect congestive heart failure (CHF) patients from normal (N) patients is described. The classification procedure uses thek-nearest neighbor algorithm and uses features from the second-order difference plot (SODP) obtained from Holter monitor cardiac RR intervals. The classification system which employs a statistical procedure to obtain the final result gave a success rate of 100% to distinguish CHF patients from normal patients. For this study the Holter monitor data of 36 normal and 36 CHF patients were used. The classification system using standard deviation of RR intervals also performed well, although it did not match the 100% success rate using the features from SODP. However, the success rate for classification using this procedure for SDRR was many fold higher compared to using a threshold. The classification system in this paper will be a valuable asset to the clinician, in the detection congestive heart failure.

scholarly journals Multi-scale Order Recurrence Plot based deterministic analysis on Heart Rate Variability in Congestive Heart Failure Assessment

2021 ◽  
Vol 271 ◽  
pp. 03063
Author(s):  
Hu Yuhang
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Normal Subjects ◽  
Recurrence Plot ◽  
Autonomic Ganglion ◽  
Autonomic Nervous ◽  
Multi Scale ◽  
Patients With Heart Failure

Congestive heart failure (CHF) is a cardiovascular disease associated with the abnormal autonomic nervous system (ANS). Heart rate variability analysis (HRV) is the main method for the quantitative evaluation of autonomic nervous function. Common analytical methods of HRV include time domain, frequency domain, and nonlinear methods. However, these methods generally ignore the short-term volatility of heart rate and autonomic ganglion law. Therefore, this study proposes a new parameter to analyze heart rate variability-determination of a multi-scale order recurrence plot (MSORP_DET). This method can analyze the HRV in patients with heart failure on multiple time scales. This study analyzed the R-R interval in 24-hour HRV data from 98 samples (54 normal subjects and 44 patients with CHF). The results showed that MSORP_DET could significantly distinguish CHF patients from normal subjects (p<0.001). Moreover, the accuracy rate of screening patients with CHF reached the maximum of 81.6% by using the combination of low frequency/high frequency (LF/HF) and MSORP_DET, compared with 78.6% when using LF/HF alone. Therefore, MSORP_DET can be used as a new index to screen patients with CHF and reveal that the rhythm of ANS in patients with heart failure is more complex than that in normal people.

scholarly journals Determination of Sample Entropy and Fuzzy Measure Entropy Parameters for Distinguishing Congestive Heart Failure from Normal Sinus Rhythm Subjects

Entropy ◽  
2015 ◽  
Vol 17 (12) ◽  
pp. 6270-6288 ◽  
Author(s):  
Lina Zhao ◽  
Shoushui Wei ◽  
Chengqiu Zhang ◽  
Yatao Zhang ◽  
Xinge Jiang ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Sample Entropy ◽  
Fuzzy Measure ◽  
Normal Sinus ◽  
Measure Entropy

scholarly journals AGE-BASED ANALYSIS OF HEART RATE VARIABILITY (HRV) FOR PATIENTS WITH CONGESTIVE HEART FAILURE

Fractals ◽  
2021 ◽  
pp. 2150135
Author(s):  
HAMIDREZA NAMAZI ◽  
DUMITRU BALEANU ◽  
ONDREJ KREJCAR
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Fractal Dimension ◽  
Age Groups ◽  
Approximate Entropy ◽  
Sample Entropy ◽  
Heart Activity ◽  
Complexity Measures

It is known that heart activity changes during aging. In this paper, we evaluated alterations of heart activity from the complexity point of view. We analyzed the variations of heart rate of patients with congestive heart failure that are categorized into four different age groups, namely 30–39, 50–59, 60–69, and 70–79 years old. For this purpose, we employed three complexity measures that include fractal dimension, sample entropy, and approximate entropy. The results showed that the trend of increment of subjects’ age is reflected in the trend of increment of the complexity of heart rate variability (HRV) since the values of fractal dimension, approximate entropy, and sample entropy increase as subjects get older. The analysis of the complexity of other physiological signals can be further considered to investigate the variations of activity of other organs due to aging.

scholarly journals Deep Ensemble Detection of Congestive Heart Failure Using Short-Term RR Intervals

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 69559-69574 ◽  
Author(s):  
Ludi Wang ◽  
Wei Zhou ◽  
Qing Chang ◽  
Jiangen Chen ◽  
Xiaoguang Zhou
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Short Term ◽  
Rr Intervals

scholarly journals Vasovagal tonus index in dog with myxomatous mitral valve disease

2017 ◽  
Vol 37 (10) ◽  
pp. 1181-1186 ◽  
Author(s):  
Bruna C. Brüler ◽  
Amália T. Giannico ◽  
Gustavo Dittrich ◽  
Marlos G. Sousa
Keyword(s):  
Internal Medicine ◽  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Mitral Valve ◽  
Cardiac Remodeling ◽  
Mitral Valve Disease ◽  
Valve Disease ◽  
Rr Intervals ◽  
Naturally Occurring

ABSTRACT: The vasovagal tonus index (VVTI) is a useful and assessable index, obtained from standard ECG recordings, that is used to estimate heart rate variability (HRV), and may provide valuable information regarding the likelihood of progression into congestive heart failure (CHF). In this paperwork, we investigated how the vasovagal tonus index (VVTI) behaves in dogs with naturally-occurring myxomatous mitral valve disease (MMVD) Electrocardiographic (ECG) recordings and echocardiographic data of 120 patients diagnosed with MMVD were reviewed. The VVTI was calculated from twenty consecutive RR intervals for each dog enrolled in the study. Lower VVTI values were found in MMVD patients in American College of Veterinary Internal Medicine (ACVIM) stage C compared with stages B1 and B2. Values were also lower in patients with severe cardiac remodeling. When a cut-off value of 6.66 is used, VVTI was able to discriminate MMVD patients in stage C from B1 and B2 dogs with a sensitivity of 70 per cent and a specificity of 77 per cent. MMVD dogs in which VVTI is lower than 6.66 are 30% more likely to develop congestive heart failure (CHF).

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