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E-raamat: Poincare Plot Methods for Heart Rate Variability Analysis

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 15-Aug-2013
  • Kirjastus: Springer-Verlag New York Inc.
  • Keel: eng
  • ISBN-13: 9781461473756
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Poincare Plot Methods for Heart Rate Variability AnalysisSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 15-Aug-2013
  • Kirjastus: Springer-Verlag New York Inc.
  • Keel: eng
  • ISBN-13: 9781461473756
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The Poincaré plot (named after Henri Poincaré) is a popular two-dimensional visualization tool for dynamic systems due to its intuitive display of the dynamic properties of a system from a time series. This book presents the basis of Poincaré plot and focus especially on traditional and new methods for analysing the geometry, temporal and spatial dynamics disclosed by the Poincaré plot to evaluate heart rate variability (HRV).

Mathematical descriptors of Poincaré plot have been developed to quantify the autonomic nervous system activity (sympathetic and parasympathetic modulation of heart rate). Poincaré plot analysis has also been used in various clinical diagnostic settings like diabetes, chronic heart failure, chronic renal failure and sleep apnea syndrome. The primary aims of quantification of the Poincaré plots are to discriminate healthy physiological systems from pathological conditions and to classify the stage of a disease. The HRV analysis by Poincaré plot has opened up ample opportunities for important clinical and research applications.

Therefore, the present book can be used either for self-study, as a supplement to courses in linear and nonlinear systems, or as a modern monograph by researchers in this field of HRV analysis.
1 Introduction
1(12)
1.1 Heart Rate Variability Techniques in Cardiology
1(2)
1.1.1 The RR Intervals
2(1)
1.2 History of Heart Rate Variability
3(2)
1.3 Physiological Basis of HRV Analysis
5(3)
1.4 Analysis Methods
8(5)
1.4.1 Time Domain
8(2)
1.4.2 Frequency Domain
10(1)
1.4.3 Nonlinear Dynamics
11(2)
2 Quantitative Poincare Plot
13(12)
2.1 Introduction
13(1)
2.2 Visualization of HRV Using Poincare Plot
14(1)
2.3 Quantification of Poincare Plot of RR Interval
15(7)
2.3.1 Ellipse-Fitting Technique
17(4)
2.3.2 Histogram Techniques
21(1)
2.4 Relationship Between Poincare Shape and Linear HRV Measure
22(1)
2.5 Conclusion
23(2)
3 Poincare Plot Interpretation of HRV Using Physiological Model
25(22)
3.1 Introduction
25(1)
3.2 Autonomous Nervous System and HRV Analysis
26(1)
3.3 Physiological HRV Model
27(3)
3.3.1 Sympathetic Oscillator
27(1)
3.3.2 Parasympathetic Respiratory Oscillator
28(1)
3.3.3 Sinus Oscillator
28(2)
3.4 Mathematical Analysis of HRV Model Using Poincare Plot
30(8)
3.4.1 Length of Poincare Plot Main Cloud
32(3)
3.4.2 Width of the Poincare Plot Main Cloud
35(2)
3.4.3 Poincare Plot Morphological Properties for the HRV Model
37(1)
3.5 Simulation Results in Clinical Examples
38(7)
3.5.1 Complete Autonomic Blockade
38(1)
3.5.2 Unopposed Sympathetic Activity--Parasympathetic Blockade
38(3)
3.5.3 Sympathetic-Parasympathetic Balance
41(1)
3.5.4 Data Set Acquisition
42(1)
3.5.5 Data Set Analysis
43(2)
3.5.6 Poincare Plot Morphology for Real Data
45(1)
3.6 Conclusion
45(2)
4 Poincare Plot in Capturing Nonlinear Temporal Dynamics of HRV
47(22)
4.1 Introduction
47(1)
4.2 Nonlinear Dynamics
48(1)
4.3 Limitation of Standard Descriptors of Poincare Plot
48(2)
4.4 Complex Correlation Measures in Poincare Plot: A Novel Nonlinear Descriptor
50(3)
4.5 Mathematical Analysis of CCM
53(4)
4.5.1 Sensitivity Analysis
53(4)
4.6 Physiological Relevance of CCM with Cardiovascular System
57(5)
4.6.1 Subjects and Study Design
57(1)
4.6.2 Results
58(2)
4.6.3 Physiological Relevance of CCM
60(2)
4.7 Clinical Case Studies Using CCM of Poincare Plot
62(3)
4.7.1 HRV Studies of Arrhythmia and Normal Sinus Rhythm
62(1)
4.7.2 HRV Studies of Congestive Heart Failure and Normal Sinus Rhythm
63(2)
4.8 Critical Remarks on CCM
65(3)
4.9 Conclusion
68(1)
5 Heart Rate Asymmetry Analysis Using Poincare Plot
69(24)
5.1 Introduction
69(1)
5.2 Existing Indices of HRA
70(3)
5.2.1 Guzik's Index
71(1)
5.2.2 Porta's Index
71(1)
5.2.3 Ehlers' Index
72(1)
5.3 New Definition of Asymmetry in RR Interval Time Series
73(2)
5.4 Modified HRA Indices Using Poincare Plot
75(2)
5.4.1 Guzik's Index (GIp)
76(1)
5.4.2 Porta's Index (PIp)
77(1)
5.4.3 Ehlers' Index (EIp)
77(1)
5.5 Application of HRA in Clinical Research
77(14)
5.5.1 Presence of HRA in Healthy Subjects
77(6)
5.5.2 Correlation Between HRA and Parasympathetic Activity
83(8)
5.6 Conclusion
91(2)
6 Segmented Poincare Plot Analysis and Lagged Segmented Poincare Plot Analysis
93(38)
6.1 Introduction
93(2)
6.2 Segmented Poincare Plot Analysis
95(15)
6.2.1 SPPA Method
95(1)
6.2.2 Applying SPPA on Simulated BBI Time Series
96(5)
6.2.3 The Ability of SPPA to Obtain Nonlinear Behaviour in Time Series When Applying Surrogate Data Analysis
101(2)
6.2.4 Application of SPPA for Risk Stratification in Dilated Cardiomyopathy Patients
103(3)
6.2.5 Investigating the Influence of Rectangle Size
106(1)
6.2.6 Investigating Age Dependencies in Healthy Subjects
107(3)
6.3 Application of SPPA to Blood Pressure Signals
110(5)
6.3.1 SPPA Adaptation to Blood Pressure (BP)
110(3)
6.3.2 Application to Hypertensive Pregnancy Disorders
113(2)
6.4 Lagged Segmented Poincare Plot Analysis
115(7)
6.4.1 Method
115(2)
6.4.2 Application of LSPPA to Determine Risk Stratification in Patients Suffering from Dilated Cardiomyopathy
117(2)
6.4.3 LSPPA in Comparison to Traditional Time and Frequency Domain Analysis
119(3)
6.5 Perspective
122(6)
6.5.1 Application of SPPA and LSPPA to Respiratory Signals
122(1)
6.5.2 Application of SPPA to Two-Dimensional Analysis of Signal Couplings (2D SPPA)
123(3)
6.5.3 Application of SPPA to Three-Dimensional Analysis of Signal Couplings (3D SPPA)
126(2)
6.6 Conclusions
128(3)
References 131(12)
Index 143
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