Empirical Mode Decomposition (EMD) analysis of HRV data from locally anesthetized patients

Engineering and Mathematical Sciences (SEMS), City University, London, UK.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:2244-7. DOI: 10.1109/IEMBS.2009.5335000
Source: PubMed


Spectral analysis of Heart Rate Variability (HRV) is used for the assessment of cardiovascular autonomic control. In this study data driven adaptive technique Empirical Mode Decomposition (EMD) and the associated Hilbert spectrum has been used to evaluate the effect of local anesthesia on HRV parameters in a group of fourteen patients undergoing brachial plexus block (local anesthesia) using transarterial technique. The confidence limit for the stopping criteria was establish and the S value that gave the smallest squared deviation from the mean was considered optimal. The normalized amplitude Hilbert spectrum was used to calculate the error index associated with the instantaneous frequency. The amplitude and the frequency values were corrected in the region where the error was higher than twice the standard deviation. The Intrinsic Mode Function (IMF) components were assigned to the Low Frequency (LF) and the High Frequency (HF) part of the signal by making use of the center frequency and the standard deviation spectral extension estimated from the marginal spectrum of the IMF components. The analysis procedure was validated with the help of a simulated signal which consisted of two components in the LF and the HF region of the HRV signal with varying amplitude and frequency. The optimal range of the stopping criterion was found to be between 4 and 9 for the HRV data. The statistical analysis showed that the LF/HF amplitude ratio decreased within an hour of the application of the brachial plexus block compared to the values at the start of the procedure. These changes were observed in thirteen of the fourteen patients included in this study.

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Available from: Panayiotis A Kyriacou, May 07, 2014
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    • "The second part, Hilbert Spectral Analysis, will generate Instantaneous Frequency. HHT has been widely used in Biomedical Engineering recently, for instance signal de-noising [2], removal of baseline wander in ECG [3], ECG detection [4], analysis of Heart Rate Variability (HRV) [5], Blood Pressure Estimation [6] and so on. "
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    ABSTRACT: Hilbert Huang Transform (HHT) is an empirical time-frequency analysis method firstly proposed by N. E. Huang in 1998. This method is suitable for nonlinear and non-stationary signal processing and thus employed for bioelectrical signal processing and analysis in recent years. However, the large computation cost of HHT restricts its applications for real-time signal processing. This paper presents a hardware accelerated HHT system based on Field Programmable Gate Array (FPGA), which employs hardware and software co-design techniques to effectively improve the processing speed.
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    ABSTRACT: 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.
    No preview · Article · Mar 2011 · IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM