scholarly journals Improving Accuracy of Heart Failure Detection Using Data Refinement

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 520
Author(s):  
Jinle Xiong ◽  
Xueyu Liang ◽  
Lina Zhao ◽  
Benny Lo ◽  
Jianqing Li ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Normal Sinus Rhythm ◽  
Characteristic Curve ◽  
Failure Detection ◽  
Original Method ◽  
Individual Variability ◽  
Data Refinement ◽  
Series Length ◽  
Rr Interval

Due to the wide inter- and intra-individual variability, short-term heart rate variability (HRV) analysis (usually 5 min) might lead to inaccuracy in detecting heart failure. Therefore, RR interval segmentation, which can reflect the individual heart condition, has been a key research challenge for accurate detection of heart failure. Previous studies mainly focus on analyzing the entire 24-h ECG recordings from all individuals in the database which often led to poor detection rate. In this study, we propose a set of data refinement procedures, which can automatically extract heart failure segments and yield better detection of heart failure. The procedures roughly contain three steps: (1) select fast heart rate sequences, (2) apply dynamic time warping (DTW) measure to filter out dissimilar segments, and (3) pick out individuals with large numbers of segments preserved. A physical threshold-based Sample Entropy (SampEn) was applied to distinguish congestive heart failure (CHF) subjects from normal sinus rhythm (NSR) ones, and results using the traditional threshold were also discussed. Experiment on the PhysioNet/MIT RR Interval Databases showed that in SampEn analysis (embedding dimension m = 1, tolerance threshold r = 12 ms and time series length N = 300), the accuracy value after data refinement has increased to 90.46% from 75.07%. Meanwhile, for the proposed procedures, the area under receiver operating characteristic curve (AUC) value has reached 95.73%, which outperforms the original method (i.e., without applying the proposed data refinement procedures) with AUC of 76.83%. The results have shown that our proposed data refinement procedures can significantly improve the accuracy in heart failure detection.

scholarly journals Preprocessing Unevenly Sampled RR Interval Signals to Enhance Estimation of Heart Rate Deceleration and Acceleration Capacities in Discriminating Chronic Heart Failure Patients from Healthy Controls

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Ping Cao ◽  
Bailu Ye ◽  
Linghui Yang ◽  
Fei Lu ◽  
Luping Fang ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chronic Heart Failure ◽  
Characteristic Curve ◽  
Clinical Analysis ◽  
Healthy Controls ◽  
Heart Failure Patients ◽  
Rr Interval ◽  
Frequency Components

Objective. The deceleration capacity (DC) and acceleration capacity (AC) of heart rate, which are recently proposed variants to the heart rate variability, are calculated from unevenly sampled RR interval signals using phase-rectified signal averaging. Although uneven sampling of these signals compromises heart rate variability analyses, its effect on DC and AC analyses remains to be addressed. Approach. We assess preprocessing (i.e., interpolation and resampling) of RR interval signals on the diagnostic effect of DC and AC from simulation and clinical data. The simulation analysis synthesizes unevenly sampled RR interval signals with known frequency components to evaluate the preprocessing performance for frequency extraction. The clinical analysis compares the conventional DC and AC calculation with the calculation using preprocessed RR interval signals on 24-hour data acquired from normal subjects and chronic heart failure patients. Main Results. The assessment of frequency components in the RR intervals using wavelet analysis becomes more robust with preprocessing. Moreover, preprocessing improves the diagnostic ability based on DC and AC for chronic heart failure patients, with area under the receiver operating characteristic curve increasing from 0.920 to 0.942 for DC and from 0.818 to 0.923 for AC. Significance. Both the simulation and clinical analyses demonstrate that interpolation and resampling of unevenly sampled RR interval signals improve the performance of DC and AC, enabling the discrimination of CHF patients from healthy controls.

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 A Novel and Effective Method for Congestive Heart Failure Detection and Quantification Using Dynamic Heart Rate Variability Measurement

PLoS ONE ◽  
2016 ◽  
Vol 11 (11) ◽  
pp. e0165304 ◽  
Author(s):  
Wenhui Chen ◽  
Lianrong Zheng ◽  
Kunyang Li ◽  
Qian Wang ◽  
Guanzheng Liu ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Failure Detection ◽  
Heart Rate Variability Measurement ◽  
Detection And Quantification

scholarly journals A New Physically Meaningful Threshold of Sample Entropy for Detecting Cardiovascular Diseases

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 830 ◽  
Author(s):  
Xiong ◽  
Liang ◽  
Zhu ◽  
Zhao ◽  
Li ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Normal Sinus Rhythm ◽  
Statistical Significance ◽  
Sample Entropy ◽  
Physiological Signals ◽  
Series Length ◽  
Popular Method ◽  
Rr Interval ◽  
Normal Sinus

Sample Entropy (SampEn) is a popular method for assessing the regularity of physiological signals. Prior to the entropy calculation, certain common parameters need to be initialized: Embedding dimension m, tolerance threshold r and time series length N. Nevertheless, the determination of these parameters is usually based on expert experience. Improper assignments of these parameters tend to bring invalid values, inconsistency and low statistical significance in entropy calculation. In this study, we proposed a new tolerance threshold with physical meaning (rp), which was based on the sampling resolution of physiological signals. Statistical significance, percentage of invalid entropy values and ROC curve were used to evaluate the proposed rp against the traditional threshold (rt). Normal sinus rhythm (NSR), congestive heart failure (CHF) as well as atrial fibrillation (AF) RR interval recordings from Physionet were used as the test data. The results demonstrated that the proposed rp had better stability than rt, hence more adaptive to detect cardiovascular diseases of CHF and AF.

Effects of angiotensin II (AT1) receptor blockade on cardiac vagal control in heart failure

2001 ◽  
Vol 101 (6) ◽  
pp. 559-566 ◽  
Author(s):  
J. C. VAILE ◽  
S. CHOWDHARY ◽  
F. OSMAN ◽  
H. F. ROSS ◽  
J. FLETCHER ◽  
...  
Keyword(s):  
Heart Failure ◽  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Angiotensin Ii ◽  
Baroreflex Sensitivity ◽  
At1 Receptor ◽  
Receptor Blockade ◽  
Double Blind ◽  
Rr Interval

The objective of the present study was to determine the autonomic effects of angiotensin II (AT1) receptor blocker therapy in heart failure. In a randomized double-blind cross-over study, we compared the effects of candesartan and placebo on baroreflex sensitivity and on heart rate variability at rest, during stress and during 24h monitoring. Acute effects were assessed 4h after oral candesartan (8mg) and chronic effects after 4 weeks of treatment (dose titrated to 16mg daily). The study group comprised 21 patients with heart failure [mean (S.E.M.) ejection fraction 33% (1%)], in the absence of angiotensin-converting enzyme (ACE) inhibitor therapy. We found that acute candesartan was not different from placebo in its effects on blood pressure or mean RR interval. Chronic candesartan significantly reduced blood pressure [placebo, 137 (3)/82 (3)mmHg; candesartan, 121 (4)/75 (2)mmHg; P<0.001; values are mean (S.E.M.)], but had no effect on mean RR interval [placebo, 857 (25)ms; candesartan, 857 (21)ms]. Compared with placebo there were no significant effects of acute or chronic candesartan on heart rate variability in the time domain and no consistent effects in the frequency domain. Baroreflex sensitivity assessed by the phenylephrine bolus method was significantly increased after chronic candesartan [placebo, 3.5 (0.5)ms/mmHg; candesartan, 4.8 (0.7)ms/mmHg; P<0.05], although there were no changes in cross-spectral baroreflex sensitivity. Thus, in contrast with previous results with ACE inhibitors, angiotensin II receptor blockade in heart failure did not increase heart rate variability, and there was no consistent effect on baroreflex sensitivity.

scholarly journals Prognostic value of heart rate variability in post-infarction patients

2014 ◽  
Vol 71 (10) ◽  
pp. 925-930 ◽  
Author(s):  
Aneta Boskovic ◽  
Natasa Belada ◽  
Bozidarka Knezevic
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Risk Stratification ◽  
Independent Predictor ◽  
Clinical Signs ◽  
Left Ventricular Ejection ◽  
Ectopic Activity ◽  
Ventricular Ectopic Activity ◽  
Rr Interval

Background/Aim. Depressed heart rate variability (HRV) indicating autonomic disequilibrium and propensity to ventricular ectopy can be useful for risk stratification in patients following acute myocardial infarction (AIM). The aim of the study was to assess heart rate variability as a predictor of allcause mortality in post-infarction patients. Methods. We analyzed the 24-hour electrocardiographic (ECG) recordings of 100 patients (80 males) during hospitalization for AIM. The mean age of patients was 56.99 + 11.03 years. Time domain heart rate variability analysis was obtained from 8 to 13 days after index infarction by mean of a 24- hour ECG recording, and the calculated parameters were: standard deviation of all normal to normal RR intervals (SDNN), RRmax-RRmin (difference between the longest RR interval and the shortest RR interval), mean RR interval. We also analyzed ventricular premature complexes from the ECG data. The patients underwent clinical evaluation, laboratory tests and echocardiography. Results. Within a oneyear follow-up period 11 patients experienced death, 10 of them because of cardiac reason and one because of stroke. There were significantly lower values of SDNN (60.55 ? 12.84 ms vs 98.38 ? 28.21 ms), RRmax-RRmin (454.36 ? 111.00 ms vs 600.99 ? 168.72 ms) and mean RR interval (695.82 ? 65.87 ms vs 840.07 ? 93.97 ms) in deceased patients than in the survivors, respectively (p < 0.01). The deceased patients were of higher mean age, with lower left ventricular ejection fraction (0.46 ? 0.05 vs 0.56 ? 0.06 in survivors), and more frequent clinical signs of heart failure and ventricular ectopic activity (> 10VPCs/h; p < 0.01). Multivariate Cox analysis showed that SDNN was a significant, independent predictor of all-cause mortality in postinfarction patients. The other independent predictors were clinical signs of heart failure - Killip class II and III and ventricular ectopic activity. Conclusion. Depressed HRV is an independent predictor of mortality in post-infarction patients and may provide useful additional prognostic information in non-invasive risk stratification of these patients.

scholarly journals Discrimination power of long-term heart rate variability measures for chronic heart failure detection

2011 ◽  
Vol 49 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Paolo Melillo ◽  
Roberta Fusco ◽  
Mario Sansone ◽  
Marcello Bracale ◽  
Leandro Pecchia
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chronic Heart Failure ◽  
Failure Detection ◽  
Discrimination Power

scholarly journals Detection of Congestive Heart Failure Based on LSTM-Based Deep Network via Short-Term RR Intervals

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1502 ◽  
Author(s):  
Ludi Wang ◽  
Xiaoguang Zhou
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Short Term Memory ◽  
Normal Sinus Rhythm ◽  
Medical Center ◽  
Segment Length ◽  
Short Term ◽  
Healthcare Applications ◽  
Rr Interval ◽  
Normal Sinus

Congestive heart failure (CHF) refers to the inadequate blood filling function of the ventricular pump and it may cause an insufficient heart discharge volume that fails to meet the needs of body metabolism. Heart rate variability (HRV) based on the RR interval is a proven effective predictor of CHF. Short-term HRV has been used widely in many healthcare applications to monitor patients’ health, especially in combination with mobile phones and smart watches. Inspired by the inception module from GoogLeNet, we combined long short-term memory (LSTM) and an Inception module for CHF detection. Five open-source databases were used for training and testing, and three RR segment length types (N = 500, 1000 and 2000) were used for the comparison with other studies. With blindfold validation, the proposed method achieved 99.22%, 98.85% and 98.92% accuracy using the Beth Israel Deaconess Medical Center (BIDMC) CHF, normal sinus rhythm (NSR) and the Fantasia database (FD) databases and 82.51%, 86.68% and 87.55% accuracy using the NSR-RR and CHF-RR databases, with N = 500, 1000 and 2000 length RR interval segments, respectively. Our end-to-end system can help clinicians to detect CHF using short-term assessment of the heartbeat. It can be installed in healthcare applications to monitor the status of human heart.

Very-low-frequency oscillations in heart rate and blood pressure in periodic breathing: role of the cardiovascular limb of the hypoxic chemoreflex

2000 ◽  
Vol 99 (2) ◽  
pp. 125-132 ◽  
Author(s):  
Darrel P. FRANCIS ◽  
L. Ceri DAVIES ◽  
Keith WILLSON ◽  
Piotr PONIKOWSKI ◽  
Andrew J. S. COATS ◽  
...  
Keyword(s):  
Heart Failure ◽  
Blood Pressure ◽  
Heart Rate ◽  
Chronic Heart Failure ◽  
Spectral Power ◽  
Low Frequency ◽  
Periodic Breathing ◽  
Free Breathing ◽  
Very Low Frequency ◽  
Rr Interval

In chronic heart failure, very-low-frequency (VLF) oscillations (0.01–0.04 Hz) in heart rate and blood pressure may be related to periodic breathing, although the mechanism has not been fully characterized. Groups of ten patients with chronic heart failure and ten healthy controls performed voluntary periodic breathing with computer guidance, while ventilation, oxygen saturation, non-invasive blood pressure and RR interval were measured. In air, voluntary periodic breathing induced periodic desaturation and prominent VLF oscillations when compared with free breathing in both patients [RR interval spectral power from 179 to 358 ms2 (P < 0.05); systolic blood pressure (SBP) spectral power from 3.44 to 6.25 mmHg2 (P < 0.05)] and controls [RR spectral power from 1040 to 2307 ms2 (P < 0.05); SBP spectral power from 3.40 to 9.38 mmHg2 (P < 0.05)]. The peak in RR interval occurred 16–26 s before that in SBP, an anti-baroreflex pattern. When the patients followed an identical breathing pattern in hyperoxic conditions to prevent desaturation, the VLF RR interval spectral power was 50% lower (179.0±51.7 ms2; P < 0.01) and the VLF SBP spectral power was 44% lower (3.51±0.77 mmHg2; P < 0.01); similar effects were seen in controls (VLF RR power 20% lower, at 1847±899 ms2, P < 0.05; VLF SBP power 61% lower, at 3.68±0.92 mmHg2, P = 0.01). Low- and high-frequency spectral powers were not significantly affected. Thus periodic breathing causes oxygen-sensitive (and by implication chemoreflex-related) anti-baroreflex VLF oscillations in RR interval and blood pressure in both patients with chronic heart failure and normal controls.

scholarly journals Fuzzy logic approach for heart rate variability

2021 ◽  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Fuzzy Logic ◽  
Frequency Domain ◽  
Time Domain ◽  
Normal Sinus Rhythm ◽  
Free Access ◽  
Massachusetts Institute Of Technology ◽  
Scientific Publications ◽  
Rr Interval

Intrioution. The heart rate variability (HRV) is based on measuring (time) intervals between R-peaks (of RR-intervals) of an electrocardiogram (ECG) and plotting a rhythmogram on their basis with its subsequent analysis by various mathematical methods that are classified as Time Domain (TD), Frequency Domain (FD) and Nonlinear (NM) [1, 2]. Diversity of methods and approaches to analysis of HRV is stemming from complexity and nonlinearity of the phenomenon itself, as well as from greater diversity of physiological reactions of an organism, both in normal and pathological states. Therefore, it appears relevant and important to incorporate currently existing HRV indicators and norms into a unified Fuzzy Logic (FL) methodology, which in turn will allow to integrally assess each metric and HRV results as a whole. Objective. We propose a Fuzzy Logic algorithm for incorporating into a single view of each metric, – Time Domain, Frequency Domain, Nonlinear Methods and HRV as a whole. Materials and methods. We define by FL the extent of belonging to normal state both for each distinct HRV metric – TD, FD and NM, and for a patient's HRV in general. Membership functions of any HRV index and defuzzification rules for FL scores was defined. In order to implement the proposed algorithm, specified parameters of mean values of HRV (М) indicators and their standard deviation (σ) have been found in scientific publications on HRV [1, 3, 7, 8, 9, 10]. We use for FL algorithm demonstration a long-term HRV records by Massachusetts Institute of Technology - Boston’s Beth Israel Hospital (MIT-BIH) from [11], a free-access, on-line archive of physiological signals for Normal Sinus Rhythm (NSR) RR Interval, Congestive Heart Failure (CHF) RR Interval and Atrial Fibrillation (AF) Databases [12]. Conclusion. In this article, we have presented a comprehensive view of HRV by Fuzzy Logic technology and thoroughly examined the peculiarities of its application and interpretation. Of all considered examples of FL analysis, the worst result is demonstrated by a patient from the AF group, while the best one belongs to a patient from the NSR group. Difference in FL Scores between these patients from NSR and CHF groups is almost 4 times, while between patients from NSR and АF groups it is almost 6 times. It appears especially important to implement such a design in portable medical devices for quick and easy interpretation of numerous parameters measured by them.

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