Bayesian spectrum estimation of unevenly sampled nonstationary data

Qi, Yuan; Minka,; Picard,

doi:10.1109/icassp.2002.1006032

Cited by 2 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A thorough comparison can be found in [20], [21], and [22], and in references therein. However, different resampling and interpolation algorithms [23], [24], [25], [26], [27] had been used to influence the outcome of the HRV analysis. Clifford and Tarassenko [26] presented a series of experiments to quantify the errors in spectral estimates of HRV due to resampling and beat replacement.…”

Section: Introductionmentioning

confidence: 99%

Hilbert-Huang Transform for Analysis of Heart Rate Variability in Cardiac Health

Kwong

Yang

et al. 2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

This paper introduces a modified technique based on Hilbert-Huang transform (HHT) to improve the spectrum estimates of heart rate variability (HRV). In order to make the beat-to-beat (RR) interval be a function of time and produce an evenly sampled time series, we first adopt a preprocessing method to interpolate and resample the original RR interval. Then, the HHT, which is based on the empirical mode decomposition (EMD) approach to decompose the HRV signal into several monocomponent signals that become analytic signals by means of Hilbert transform, is proposed to extract the features of preprocessed time series and to characterize the dynamic behaviors of parasympathetic and sympathetic nervous system of heart. At last, the frequency behaviors of the Hilbert spectrum and Hilbert marginal spectrum (HMS) are studied to estimate the spectral traits of HRV signals. In this paper, two kinds of experiment data are used to compare our method with the conventional power spectral density (PSD) estimation. The analysis results of the simulated HRV series show that interpolation and resampling are basic requirements for HRV data processing, and HMS is superior to PSD estimation. On the other hand, in order to further prove the superiority of our approach, real HRV signals are collected from seven young health subjects under the condition that autonomic nervous system (ANS) is blocked by certain acute selective blocking drugs: atropine and metoprolol. The high-frequency power/total power ratio and low-frequency power/high-frequency power ratio indicate that compared with the Fourier spectrum based on principal dynamic mode, our method is more sensitive and effective to identify the low-frequency and high-frequency bands of HRV.

show abstract

Section: Introductionmentioning

confidence: 99%

Hilbert-Huang Transform for Analysis of Heart Rate Variability in Cardiac Health

Kwong

Yang

et al. 2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

show abstract

“…A very similar method was proposed in [4], except that localization in [4] is achieved with input noise rather than with a damping factor γ. The results of the present paper allow to use this PLL also for the local detection of a sinusoid at unknown signal-to-noise ratio.…”

Section: Example 2 (Pll and Detection Of A Sinusoid) Letmentioning

confidence: 99%

“…Note that Examples 1 and 2 are meaningfull both with offline estimation as in (3) and with online estimation as in (4).…”

Section: Example 2 (Pll and Detection Of A Sinusoid) Letmentioning

confidence: 99%

Local statistical models from deterministic state space models, likelihood filtering, and local typicality

Bruderer

Loeliger

Zalmai

2014

2014 IEEE International Symposium on Information Theory

View full text Add to dashboard Cite

show abstract

Bayesian spectrum estimation of unevenly sampled nonstationary data

Cited by 2 publications

References 0 publications

Hilbert-Huang Transform for Analysis of Heart Rate Variability in Cardiac Health

Hilbert-Huang Transform for Analysis of Heart Rate Variability in Cardiac Health

Local statistical models from deterministic state space models, likelihood filtering, and local typicality

Contact Info

Product

Resources

About