Estimating spectral heart rate variability (HRV) features with missing RR-interval data
Surat-E-Mostafa, Md; Reza, Al Faisal; Mostafa, Sakib; Datta, Metali Rani; Rupa, Rawshon Ara (2020-03-19)
M. Surat-E-Mostafa, M. A. Faisal Reza, S. Mostafa, M. R. Datta and R. Ara Rupa, "Estimating spectral heart rate variability (HRV) features with missing RR-interval data," 2019 22nd International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 2019, pp. 1-6, doi: 10.1109/ICCIT48885.2019.9038387
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2020110989733
Tiivistelmä
Abstract
Physiological signals, ECG signal, have been widely used for diagnosis, disease identification and nowadays for self-monitoring. Missing data represents the problem in spectral analysis. This study focuses on the HRV power spectral analysis in frequency-domain using three methods with simulated missing data in real RR-interval tachograms. Actual missing ECG data are collected from the unconstrained measurement. Parametric, Non-parametric and uneven sampling approach were used for calculating the power spectral density (PSD), and cubic spline interpolation method was used for the non-parametric method. Based on this studies outcome, the effect of missing RR-interval data and optimal method was observed through the simulated real RR-interval tachograms for missing data. About 0 to 6 percentage data were removed according to the exponential Poisson distribution from the real RR-interval data for normal sinus rhythm, atrial fibrillation, tachycardia and bradycardia patient which data obtained from MIT-BIH Arrhythmia database to simulate real-world packet loss. For this analysis, 5 min duration data were used in all and 1000 Monte Carlo runs is performed for certain percentage missing data. PSD corresponding each frequency component was estimated as the frequency-domain parameters in each run and error power percentage based on each element difference between with and without the missing data duration were calculated.
Kokoelmat
- Avoin saatavuus [31928]