Determination of Maximum Noise Level in an ECG Channel Under SURE Wavelet Filtering for HRV Extraction
DOI:
https://doi.org/10.17488/RMIB.41.2.5Keywords:
HRV, SNR, Wavelet, ECG filteringAbstract
Heart Rate Variability (HRV) is the measure of variation between R-R interbeats, it has been demonstrated to be a good representation of physiological features, especially to the alterations in the Autonomic Nervous System (ANS). Considering the values that compose a HRV distribution are extracted from electrocardiography (ECG), many of the electrical disturbances that affect ECG-based diagnosis can also interfere with the results of the HRV analysis. This paper uses a 30-minute portion of a healthy patient (no arrhythmias detected or annotated) from the MIT-BIH ECG database to analyze the effectiveness of the SURE Wavelet denoising method for extracting the HRV from a progres- sively noisier ECG channel. Results show that the minimum SNR for reliable HRV extraction under these conditions is approximately 5dB and outlines the exponential behavior of HRV extraction for escalating noise levels in the ECG signal.
Downloads
References
Mendis S, Puska P, Norrving B. Global atlas on cardiovascular disease prevention and control [Internet]. Geneva: World Health Organization; 2011. Available from: https://www.who.int/cardiovascular_diseases/publications/atlas_cvd/en/
Hall JE. Guyton and Hall textbook of medical physiology e-Book. 12th edition. Philadelphia: Elsevier Health Sciences; 2010.
Bayés De Luna A. Basic electrocardiography: normal and abnormal ECG patterns. Hershey, PA: John Wiley & Sons; 2008. doi:10.1002/9780470692622
Freeman R, Saul JP, Roberts MS, Berger RD, Broadbridge C, Cohen RJ. Spectral analysis of heart rate in diabetic autonomic neuropathy: a comparison with standard tests of autonomic function. Archives of Neurology. 1991;48(2):185-90. doi:10.1001/archneur.1991.00530140079020
Rothschild M, Rothschild A, Pfeifer M. Temporary decrease in cardiac parasympathetic tone after acute myocardial infarction. The American journal of cardiology. 1988;62(9):637-9. doi.org/10.1016/0002-9149(88)90670-4
Page A, Hassanalieragh M, Soyata T, Aktas MK, Kantarci B, Andreescu S. Conceptualizing a real-time remote cardiac health monitoring system. In Medical Imaging: Concepts, Methodologies, Tools, and Applications. Hershey, PA: IGI Global; 2017. p.160-193. doi: 10.4018/978-1-5225-0571-6
Joshi SL, Vatti RA, Tornekar RV. A survey on ECG signal denoising techniques. 2013 International Conference on Communication Systems and Network Technologies. Gwalior, India: IEEE; 2013. p. 60-64. doi.org/10.1109/CSNT.2013.22
Zhang D. Wavelet approach for ECG baseline wander correction and noise reduction. 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. Shanghai: IEEE; 2005. p. 1212-1215. doi: 10.1109/IEMBS.2005.1616642.
Allisy-Roberts PJ, Williams J. Farr's physics for medical imaging. 2nd edition. Europe: Saunders- Elsevier; 2008.
Sijbers J, Scheunders P, Bonnet N, Van Dyck D, Raman E. Quantification and improvement of the signal-to-noise ratio in a magnetic resonance image acquisition procedure. Magnetic resonance imaging. 1996;14(10):1157-63. https://doi.org/10.1016/S0730-725X(96)00219-6
Johnson DH. Signal-to-noise ratio. Scholarpedia [Internet]. 2006:1(12): 2088. Available from: http://www.scholarpedia.org/article/Signal-to-noise_ratio
Srivastava M, Anderson CL, Freed JH. A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds. IEEE Access. 2016;4:3862-77. doi: 10.1109/ACCESS.2016.2587581.
Antoniadis A. Wavelets in statistics: a review. Journal of the Italian Statistical Society. 1997; 6: 97. https://doi.org/10.1007/BF03178905
Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23): E215-E220. doi:10.1161/01.cir.101.23.e215
Moody GB, Mark RG. The MIT-BIH arrhythmia database on CD-ROM and software for use with it. Proceedings Computers in Cardiology. Chicago: IEEE; 1990. p. 185-188. https://doi.org/ 10.1109/CIC.1990.144205
Sedghamiz H. BioSigKit: A Matlab Toolbox and Interface for Analysis of BioSignals. Journal of Open Source Software. 2018;3(30): 671. https://doi.org/10.21105/joss.00671
Geetha G, Geethalakshmi SN. EEG De-noising using SURE Thresholding based on Wavelet Transforms. International Journal of Computer Applications. 2011;24(6):29-33. https://doi.org/10.5120/2948-3935
Rodríguez-Liñares L, Lado MJ, Vila XA, Méndez AJ, Cuesta P. gHRV: Heart rate variability analysis made easy. Computer methods and programs in biomedicine. 2014;116(1):26-38 doi: 10.1016/j.cmpb.2014.04.007
Hsu CH, Tsai MY, Huang GS, Lin TC, Chen KP, Ho ST, Shyu LY, Li CY. Poincaré plot indexes of heart rate variability detect dynamic autonomic modulation during general anesthesia induction. Acta Anaesthesiologica Taiwanica. 2012;50(1):12-8. https://doi.org/10.1016/j.aat.2012.03.002
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Revista Mexicana de Ingeniería Biomédica
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Upon acceptance of an article in the RMIB, corresponding authors will be asked to fulfill and sign the copyright and the journal publishing agreement, which will allow the RMIB authorization to publish this document in any media without limitations and without any cost. Authors may reuse parts of the paper in other documents and reproduce part or all of it for their personal use as long as a bibliographic reference is made to the RMIB. However written permission of the Publisher is required for resale or distribution outside the corresponding author institution and for all other derivative works, including compilations and translations.
