Segmented Poincaré Plot Analysis for Risk Stratification in Patients with Dilated Cardiomyopathy

2010 ◽  
Vol 49 (05) ◽  
pp. 511-515 ◽  
Author(s):  
C. Fischer ◽  
R. Schroeder ◽  
H. R. Figulla ◽  
M. Goernig ◽  
A. Voss
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Risk Stratification ◽  
Dilated Cardiomyopathy ◽  
Heart Rate Variability Analysis ◽  
Poincaré Plot ◽  
Plot Analysis ◽  
Interval Time Series ◽  
Nonlinear Features ◽  
Cloud Of Points

Summary Background: The prognostic value of heart rate variability in patients with dilated cardiomyopathy (DCM) is limited and does not contribute to risk stratification although the dynamics of ventricular repolarization differs considerably between DCM patients and healthy subjects. Neither linear nor nonlinear methods of heart rate variability analysis could discriminate between patients at high and low risk for sudden cardiac death. Objective: The aim of this study was to analyze the suitability of the new developed segmented Poincaré plot analysis (SPPA) to enhance risk stratification in DCM. Methods: In contrast to the usual applied Poincaré plot analysis the SPPA retains nonlinear features from investigated beat-to-beat interval time series. Main features of SPPA are the rotation of cloud of points and their succeeded variability depended segmentation. Results: Significant row and column probabilities were calculated from the segments and led to discrimination (up to p < 0.005) between low and high risk in DCM patients. Conclusion: For the first time an index from Poincaré plot analysis of heart rate variability was able to contribute to risk stratification in patients suffering from DCM.

Characterization of HRV by Poincare Plot Analysis among the Female Tea Garden Workers of Northern Hilly Regions of West Bengal

2012 ◽  
pp. 206-215
Author(s):  
Somsirsa Chatterjee ◽  
Ankur Ganguly ◽  
Saugat Bhattacharya
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
West Bengal ◽  
Mean Value ◽  
Short Term ◽  
Poincaré Plot ◽  
Plot Analysis ◽  
Tea Garden

Recent research on Heart Rate Variability (HRV) has proven that Poincare Plot is a powerful tool to mark Short Term and Long Term Heart Rate Variability. This study focuses a comprehensive characterization of HRV among the Tea Garden Workers of the Northern Hilly Regions of West Bengal. The characterization, as available from the data sets, projects the average values of SD1 characteristics, that is, Short Term HRV in females as 58.265ms and SD2 as 149.474. The SDRR shows a mean value of 87.298 with a standard deviation of 119.669 and the S Characterization as 16505.99 ms and Standard deviation of 45882.31 ms. The SDRR shows a mean value of 87.298 with a standard deviation of 119.669 and the S Characterization as 16505.99 ms and Standard deviation of 45882.31 ms. ApEn Characterization showed mean value of 0.961 and standard deviation of 0.274.

Multiparametric Heart Rate Variability Analysis for High Risk Stratification after Myocardial Infarction

1996 ◽  
Vol 91 (s1) ◽  
pp. 118-119 ◽  
Author(s):  
Andreas Voss ◽  
Juergen Kurths ◽  
Niels Wessel ◽  
Annette Witt ◽  
Hans J. Kleiner ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
High Risk ◽  
Risk Stratification ◽  
Heart Rate Variability Analysis ◽  
Variability Analysis

scholarly journals Heart rate variability analysis based on modified Poincaré plot

2012 ◽  
Vol 61 (19) ◽  
pp. 190506
Author(s):  
Huo Cheng-Yu ◽  
Zhuang Jian-Jun ◽  
Huang Xiao-Lin ◽  
Hou Feng-Zhen ◽  
Ning Xin-Bao
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Heart Rate Variability Analysis ◽  
Poincaré Plot ◽  
Variability Analysis

scholarly journals Heart Rate Variability Analysis and The Influence of Exercise Intensity Over Time in Young-healthy Women

2021 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
Aisha Widi Rahayu ◽  
Izza Alifa Hassya ◽  
Eki Dipo Laksono ◽  
Alvin Sahroni
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Exercise Intensity ◽  
Circulatory System ◽  
Heart Rate Variability Analysis ◽  
Regular Exercise ◽  
Poincaré Plot ◽  
Evaluation Period ◽  
Healthy Women ◽  
Exercise Period

Our heart is a vital organ that pumps blood and through the vessels of the circulatory system. In medical applications, we can observe the heart rate using Electrocardiograph (ECG). Currently, people tend to have high working activity without a proper exercise intensity. This study was conducted to observe the heart rate variability (HRV) on the healthy young woman who was not doing any exercise. We evaluated the HRV characteristics while exercising with a regular period and different intensity (light to hard) and how the difference before and after of evaluation period. Seven young-healthy women (19 - 21 years old) women were observed during three observation stages: pre-exercise, main exercise-period, and post-exercise for 2 months. We analyzed MeanRR, SDRR, CVRR, rMSSD, VLF, LF, HF, and the Poincaré plot parameters (SD1 and SD2) as the HRV properties. We found that SDRR was decreased from the first week (0.08 s) to the last week of the evaluation period (0.03 s) followed by the HF component (0.15 – 0.2 Hz). The Poincaré plot properties also reduced from the first week to the last week of the exercise period (0.07 s to 0.03 s). We indicated the characteristics of a woman's HRV during regular exercise periods with different intensity have made the heart more effective in pumping blood. We concluded that the heart condition would be improved during regular exercise with the increment of intensity even in a short of a period. Finally, the heart rate performance may be decreased during absent from regular exercise for a month.

FETAL HEART RATE VARIABILITY ANALYSIS FROM PHONOCARDIOGRAPHIC RECORDINGS

2011 ◽  
Vol 11 (05) ◽  
pp. 1315-1331 ◽  
Author(s):  
VIJAY S. CHOURASIA ◽  
ANIL KUMAR TIWARI
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Health Status ◽  
Fetal Heart Rate ◽  
Heart Sound ◽  
Fetal Heart ◽  
Heart Rate Variability Analysis ◽  
Nonlinear Features ◽  
Fetal Heart Rate Variability ◽  
Fetal Wellbeing

This paper presents an algorithm for classification of fetal health status using fetal heart rate variability (fHRV) analysis through phonocardiography. First, the fetal heart sound signals are acquired from the maternal abdominal surface using a specially developed Bluetooth-based wireless data recording system. Then, fetal heart rate (FHR) traces are derived from these signals. Ten numbers of linear and nonlinear features are extracted from each FHR trace. Finally, the multilayer perceptron (MLP) neural network is used to classify the health status of the fetus. Results show very promising performance toward the prediction of fetal wellbeing on the set of collected fetal heart sound signals. Finally, this work is likely to lead to an automatic screening device with additional potential of predicting fetal wellbeing.

Lagged segmented Poincaré plot analysis for risk stratification in patients with dilated cardiomyopathy

2012 ◽  
Vol 50 (7) ◽  
pp. 727-736 ◽  
Author(s):  
Andreas Voss ◽  
Claudia Fischer ◽  
Rico Schroeder ◽  
Hans R. Figulla ◽  
Matthias Goernig
Keyword(s):  
Risk Stratification ◽  
Dilated Cardiomyopathy ◽  
Poincaré Plot ◽  
Plot Analysis

scholarly journals A novel approach in heart-rate-variability analysis

2021 ◽  
Author(s):  
Andras Buzas ◽  
Tamas Horvath ◽  
Andras Der
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Extended Period ◽  
Evaluation Procedure ◽  
Heart Rate Variability Analysis ◽  
Daily Routine ◽  
Poincaré Plot ◽  
Statistical Measures ◽  
Registration Period

Heart-rate variability (HRV), measured by the fluctuation of beat-to-beat intervals, has been growingly considered the most important hallmark of heart rate (HR) time series. HRV can be characterized by various statistical measures both in the time and frequency domains, or by nonlinear methods. During the past decades, an overwhelming amount of HRV data has been piled up in the research community, but the individual results are difficult to reconcile due to the different measuring conditions and the usually HR-dependent statistical HRV-parameters applied. Moreover, the precise HR-dependence of HRV parameters is not known. Using data gathered by a wearable sensor of combined heart-rate and actigraphy modalities, here, we introduce a novel descriptor of HRV, based on a modified Poincare plot of 24-h RR-recordings. We show that there exists a regressive biexponential HRV versus HR master curve (M-curve) that is highly conserved for a healthy individual on short and medium terms (on the hours to months scale, respectively). At the same time, we reveal how this curve is related to age in the case of healthy people, and establish alterations of the M-curves of heart-attack patients. A stochastic neuron model accounting for the observed phenomena is also elaborated, in order to facilitate physiological interpretation of HRV data. Our novel evaluation procedure applied on the time series of interbeat intervals allows the description of the HRV(HR) function with unprecedented precision. To utilize the full strength of the method, we suggest a 24-hour-long registration period under natural, daily-routine circumstances (i.e., no special measuring conditions are required). By establishing a patient's M-curve, it is possible to monitor the development of his/her status over an extended period of time. On these grounds, the new method is suggested to be used as a competent tool in future HRV analyses for both clinical and training applications, as well as for everyday health promotion.

NOVEL CLASSIFICATION OF CORONARY ARTERY DISEASE USING HEART RATE VARIABILITY ANALYSIS

2012 ◽  
Vol 12 (04) ◽  
pp. 1240017 ◽  
Author(s):  
SUMEET DUA ◽  
XIAN DU ◽  
S. VINITHA SREE ◽  
THAJUDIN AHAMED V. I.
Keyword(s):  
Coronary Artery Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Coronary Artery ◽  
Principal Component ◽  
Heart Rate Variability Analysis ◽  
Fluctuation Analysis ◽  
Non Invasive ◽  
Nonlinear Features ◽  
Artery Disease

Coronary artery disease (CAD) is a leading cause of death worldwide. Heart rate variability (HRV) has been proven to be a non-invasive marker of the autonomic modulation of the heart. Nonlinear analyses of HRV signals have shown that the HRV is reduced significantly in patients with CAD. Therefore, in this work, we extracted nonlinear features from the HRV signals using the following techniques: recurrence plots (RP), Poincare plots, and detrended fluctuation analysis (DFA). We also extracted three types of entropy, namely, Shannon entropy (ShanEn), approximation entropy (ApEn), and sample entropy (SampEn). These features were subjected to principal component analysis (PCA). The significant principal components were evaluated using eight classification techniques, and the performances of these techniques were evaluated to determine which presented the highest accuracy in classifying normal and CAD classes. We observed that the multilayer perceptron (MLP) method resulted in the highest classification accuracy (89.5%) using our proposed technique.

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