University of Oulu

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

Estimating spectral heart rate variability (HRV) features with missing RR-interval data

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Author: Surat-E-Mostafa, Md1; Reza, Al Faisal2; Mostafa, Sakib3;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
2European University of Bangladesh, Dhaka, Bangladesh
3University of Rajshahi, Rajshahi, Bangladesh
4M Abdur Rahim Medical College, Dinajpur, Bangladesh
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-11-09


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.

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ISBN: 978-1-7281-5842-6
ISBN Print: 978-1-7281-5843-3
Pages: 1 - 6
Article number: 9038387
DOI: 10.1109/ICCIT48885.2019.9038387
Host publication: 22nd International Conference on Computer and Information Technology, ICCIT 2019
Conference: International Conference on Computer and Information Technology
Type of Publication: A4 Article in conference proceedings
Field of Science: 216 Materials engineering
213 Electronic, automation and communications engineering, electronics
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