Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

An approach to diagnose cardiac conditions from electrocardiogram signals.

Lu, Yan."October 2010."Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (leaves 65-68).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1. --- Introduction --- p.1Chapter 1.1 --- Electrocardiogram --- p.1Chapter 1.1.1 --- ECG Measurement --- p.2Chapter 1.1.2 --- Cardiac Conduction Pathway and ECG Morphology --- p.4Chapter 1.1.3 --- A Basic Clinical Approach to ECG Analysis --- p.6Chapter 1.2 --- Cardiovascular Disease --- p.7Chapter 1.3 --- Motivation --- p.9Chapter 1.4 --- Related Work --- p.10Chapter 1.5 --- Overview of Proposed Approach --- p.11Chapter 1.6 --- Thesis Outline --- p.13Chapter 2. --- ECG Signal Preprocessing --- p.14Chapter 2.1 --- ECG Model and Its Generalization --- p.14Chapter 2.1.1 --- ECG Dynamic Model --- p.14Chapter 2.1.2 --- Generalization of ECG Model --- p.15Chapter 2.2 --- Empirical Mode Decomposition --- p.17Chapter 2.3 --- Baseline Wander Removal --- p.20Chapter 2.3.1 --- Sources of Baseline Wander --- p.20Chapter 2.3.2 --- Baseline Wander Removal by EMD --- p.20Chapter 2.3.3 --- Experiments on Baseline Wander Removal --- p.21Chapter 2.4 --- ECG Denoising --- p.24Chapter 2.4.1 --- Introduction --- p.24Chapter 2.4.2 --- Instantaneous Frequency --- p.26Chapter 2.4.3 --- Problem of Direct ECG Denoising by EMD : --- p.28Chapter 2.4.4 --- Model-based Pre-filtering --- p.30Chapter 2.4.5 --- EMD Denoising Using Significance Test --- p.33Chapter 2.4.6 --- EMD Denoising using Instantaneous Frequency --- p.35Chapter 2.4.7 --- Experiments --- p.39Chapter 2.5 --- Chapter Summary --- p.44Chapter 3. --- ECG Classification --- p.45Chapter 3.1 --- Database --- p.45Chapter 3.2 --- Feature Extraction --- p.46Chapter 3.2.1 --- Feature Selection --- p.46Chapter 3.2.2 --- Feature Dimension Reduction by GDA --- p.48Chapter 3.3 --- Classification by Support Vector Machine --- p.50Chapter 3.4 --- Experiments --- p.53Chapter 3.4.1 --- Performance of Feature Reduction --- p.54Chapter 3.4.2 --- Performance of Classification --- p.57Chapter 3.4.3 --- Performance Comparison with Other Works --- p.60Chapter 3.5 --- Chapter Summary --- p.61Chapter 4. --- Conclusions --- p.63Reference --- p.6