Modeling and decomposition of HRV signals with wavelet transforms

Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal

Previous studies have attempted to find autonomic differences of the cardiac system between the congestive heart failure (CHF) disease and healthy groups using a variety of algorithms of pattern recognition. By comparing previous literature, we have found that there are two shortcomings: 1) Previous studies have focused on improving the accuracy of models, but the number of features used has mostly exceeded 10, leading to poor generalization performance; 2) Previous works rarely distinguish the severity levels of CHF disease. In order to make up for these two shortcomings, we proposed two models: model A was used for distinguishing CHF patients from the normal people; model B was used for diagnosing the four severity levels of CHF disease. Based on long-term heart rate variability (HRV) (40000 intervals–8h) signals, we extracted linear and non-linear features from the inter-beat-interval (IBI) series. After that, the sequence forward selection algorithm (SFS) reduced the feature dimension. Finally, models with the best performance were selected through the leave-one-subject-out validation. For a total of 113 samples of the dataset, we applied the support vector machine classifier and five HRV features for CHF discrimination and obtained an accuracy of 97.35%. For a total of 41 samples of the dataset, we applied k-nearest-neighbor (K = 1) classifier and four HRV features for diagnosing four severity levels of CHF disease and got an accuracy of 87.80%. The contribution in this work was to use the fewer features to optimize our models by the leave-one-subject-out validation. The relatively good generalization performance of our models indicated their value in clinical application.

Improved HRV characterization using OCDWT

In this paper an over-complete discrete wavelet transform (OCDWT) algorithm, obtained by blending two wavelet transform implementations that is redundant wavelet transform and the Mallat's multiresolution decomposition, has been proposed to retrieve the time-varying characteristics of HRV under two different postures, supine and standing. The OCDWT algorithm is critically sub-sampled to a given level of decomposition, below which it is then fully sampled. Five subjects were included to investigate posture-related HRV. The results showed that the high frequency fluctuations are larger in supine and get significantly reduced in standing in comparison to low frequency variations. Moreover, the very low frequency heart beat fluctuations during supine were greater than during standing. Further a comparative analysis has also been made between the Mallat's and OCDWT implementation in order to show the superiority of proposed algorithm.

Wavelet packet transform for R-R interval variability

INTRODUCTION

Wavelet transform is used for time-frequency analysis. Recently, discrete wavelet transform (DWT) has been used to analyze R-R interval or heart rate variability. However, we hypothesized that wavelet packet transform (WPT) is a better way to analyze such variability. In the present study, we compared resolution of frequency band and amplitude, which are used for analysis of the variability, with DWT and WPT, followed by Hilbert transform.

METHODS

A chirp signal which covers all frequency bands used for R-R interval variability was employed as a simulated signal. Levels 1-6 of DWT and level 3 of WPT were used for signal analysis. Amplitudes of the gained signal were evaluated with Hilbert transform. Differences in error of the gained amplitude from expected amplitude between CWT and DWT for low-frequency (LF) and high-frequency (HF) components were compared. To evaluate time-dependent changes in R-R interval variability, head-up tilt (HUT) was employed as an orthostatic challenge.

RESULTS

Errors for both HF and LF, derived from the simulated signal with WPT, were significantly smaller than those of DWT. With HUT, time dependent changes in LF, HF, and LF/HF were observed.

DISCUSSION

Although DWT is a valuable method for time-frequency analysis, WPT is a more appropriate method to utilize wavelet transform due to the equivalent resolution of the gained frequency band. WPT for time-frequency analysis improves analysis of time-dependent changes in R-R interval variability.

Wavelet transform analysis of heart rate variability during myocardial ischaemia

Analysis of heart rate variability (HRV) is a valuable, non-invasive method for quantifying autonomic cardiac control in humans. Frequency-domain analysis of HRV involving myocardial ischaemic episodes should take into account its non-stationary behaviour. The wavelet transform is an alternative tool for the analysis of non-stationary signals. Fourteen patients have been analysed, ranging from 40 to 64 years old and selected from the European Electrocardiographic ST-T Database (ESDB). These records contain 33 ST episodes, according to the notation of the ESDB, with durations of between 40s and 12 min. A method for analysing HRV signals using the wavelet transform was applied to obtain a time-scale representation for very low-frequency (VLF), low-frequency (LF) and high-frequency (HF) bands using the orthogonal multiresolution pyramidal algorithm. The design and implementation using fast algorithms included a specially adapted decomposition quadrature mirror filter bank for the frequency bands of interest. Comparing a normality zone against the ischaemic episode in the same record, increases in LF (0.0112 +/- 0.0101 against 0.0175 +/- 0.0208 s2 Hz(-1); p<0.1) and HF (0.0011 +/- 0.0008 against 0.00 17 +/- 0.0020 s2 Hz(-1); p<0.05) were obtained. The possibility of using these indexes to develop an ischaemic-episode classifier was also tested. Results suggest that wavelet analysis provides useful information for the assessment of dynamic changes and patterns of HRV during myocardial ischaemia.

Accurate identification of periodic oscillations buried in white or colored noise using fast orthogonal search

We use a previously introduced fast orthogonal search algorithm to detect sinusoidal frequency components buried in either white or colored noise. We show that the method outperforms the correlogram, modified covariance autoregressive (MODCOVAR) and multiple-signal classification (MUSIC) methods. Fast orthogonal search method achieves accurate detection of sinusoids even with signal-to-noise ratios as low as -10 dB, and is superior at detecting sinusoids buried in 1/f noise. Since the utilized method accurately detects sinusoids even under colored noise, it can be used to extract a 1/f noise process observed in physiological signals such as heart rate and renal blood pressure and flow data.