Literature DB >> 19965157

Empirical mode decomposition (EMD) analysis of HRV data from locally anesthetized patients.

K Shafqat1, S K Pal, S Kumari, P A Kyriacou.   

Abstract

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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Year:  2009        PMID: 19965157     DOI: 10.1109/IEMBS.2009.5335000

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  2 in total

1.  Robust muscle force prediction using NMFSEMD denoising and FOS identification.

Authors:  Yuan Wang; Fan Li; Haoting Liu; Zhiqiang Zhang; Duming Wang; Shanguang Chen; Chunhui Wang; Jinhui Lan
Journal:  PLoS One       Date:  2022-08-03       Impact factor: 3.752

2.  Influence of Sliding Time Window Size Selection Based on Heart Rate Variability Signal Analysis on Intelligent Monitoring of Noxious Stimulation under Anesthesia.

Authors:  Qiang Yin; Dai Shen; Qian Ding
Journal:  Neural Plast       Date:  2021-06-05       Impact factor: 3.599

  2 in total

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