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Reseach Article
Impact of Missing RR-interval on Non-Linear HRV Parameters
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 124 - Number 11 |
| Year of Publication: 2015 |
| Authors: Manjit Singh, Butta Singh, Ashwani Rajput |
10.5120/ijca2015905641
|
Manjit Singh, Butta Singh, Ashwani Rajput . Impact of Missing RR-interval on Non-Linear HRV Parameters. International Journal of Computer Applications. 124, 11 ( August 2015), 13-18. DOI=10.5120/ijca2015905641
Abstract
The impact of missing RR-interval data on nonlinear heart rate variability (HRV) analysis with and without interpolation were investigated. In this study, randomly selected data (with frequency of 5 samples up to 50) were removed from actual data (taking first 1000 samples) in the MIT-BIH arrhythmia RR interval database of 10 subjects having 1000 sample data points in each set. In all, the tachograms the artefacts are removed first from the 1000 samples. Poincare plot and entropy analysis were executed for the nonlinear HRV parameters, and the absolute relative errors between the data with and without the missing data duration for these parameters including the interpolation were calculated. In this process, the usefulness of reconstruction was considered when there is missed rr-interval, for which several interpolation methods (linear, delete, and zero order interpolation) were used and the best interpolation method having less error in the HRV analysis was chosen. During the work and performing all the interpolation methods, the delete interpolation gives best results for the reconstruction of data while analysing the HRV non-linear parameters.
References
- K. K. Kima, H. J. Baek, Y. G. Limb, K. S. Park, “Effect of missing RR-interval data on nonlinear heart rate variability analysis,” Comput Methods Programs Biomed Elsevier journals, 2012
- P. Guerrero, C. Mailhes, and F. Castanié, “Lost Sample Recovering of ECG Signals in e-Health Applications,” Engineering in Medicine and Biology Society, 29th Annual International Conference of the IEEE,pp. 31 - 34, 2007
- M. Brennan, M. Palaniswami, and P. Kamen, “Do Existing Measures of Poincare Plot Geometry Reflect Nonlinear Features of Heart Rate Variability,” Engineering in Medicine and Biology Society, Annual International Conference of the IEEE,pp. 1342 - 1347, 2001
- G. Ganeshapillai, J. F. Liu, and J. Guttag, “Reconstruction of ECG Signals in The Presence of Corruption,” Engineering in Medicine and Biology Society, Annual International Conference of the IEEE,pp. 3764 - 3767, 2011
- S. Pincus, Approximate entropy (Apen) as a complexity measure, CHAOS 5 (1995).pp. 110–117.
- M. Aboy, D. Cuesta-Frau, D. Austin and P. Mico´-Tormos, “Characterization of Sample Entropy in the Context of Biomedical Signal Analysis,” Engineering in Medicine and Biology Society, Annual International Conference of the IEEE,pp. 5942 - 5945, 2007
- C. Lerma, O. Infante, H. Perez-Grovas and M. V. Jose, “Poincare plot indexes of heart rate variability capture dynamic adaptations after haemodialysis in chronic renal failure patients,” Clin Physiol Funct Imaging,pp. 72-80, 2003
- B. Efron, Missing data, imputation, and the bootstrap, J. Am. Stat. Assoc. 89 (1994) 463–475
- J.S. Richman, J.R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy, Am. J. Physiol.-Heart C 278 (2000) H2039–H2049
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