QRS Detection On Heart Rate Variability Readings using Two Moving Average Methods

  • Ayu Nissa Berlianri Rizhky Department of Medical Electronics Technology, Poltekkes Kemenkes Surabaya
  • I Dewa Gede Hari Wisana Department of Medical Electronics Technology, Poltekkes Kemenkes Surabaya https://orcid.org/0000-0003-3497-2230
  • Sima Das Camellia Institute of Technology and Management, Hooghly, India https://orcid.org/0000-0001-8048-6597

Abstract

Heart Rate Variability or heart rate in humans is used to unify the heart rate in humans, the function of the heart rate monitor is used to unite the human heart rate. The purpose of making this tool is to read the human heart rate using the Two Moving Average method or moving average which makes it easy to find the R peak to peak signal, making it easier to read. This is achieved by discovering and studying each window size change specified by, so that it can be seen the change in every two moving averages for each window size value. This study uses the Arduino Nano system for data processing and uses Delphi to display the processed data. In this study examined signaling and heart rate monitoring for 5 minutes. In this study it can be said that, the best window size with the best signal results for measuring heart rate is the window size 15 . this method is a method with a good accuracy rate of 98%. And also this method can be displayed directly by displaying the RR Interval and HRV value for 10 minutes with results close to 0. This method is recommended to detect high enough P and T signal.

Downloads

Download data is not yet available.

References

R. R. Chamley, D. A. Holdsworth, K. Rajappan, and E. D. Nicol, “ECG interpretation,” Eur. Heart J., vol. 40, no. 32, pp. 2663–2666, 2019, doi: 10.1093/eurheartj/ehz559.

S. Setiawidayat, D. Sargowo, S. P. Sakti, and S. Andarini, “The peak of the PQRST and the trajectory path of each cycle of the ECG 12-lead wave The Peak of the PQRST and the Trajectory Path of Each Cycle of the ECG 12-Lead Wave,” no. December 2018, pp. 169–175, 2016, doi: 10.11591/ijeecs.v4.i1.pp169-175.

G. D. Gargiulo et al., “On the Einthoven Triangle : A Critical Analysis of the Single Rotating Dipole Hypothesis,” no. Ll, 2006, doi: 10.3390/s18072353.

S. Torbey, S. G. Akl, and D. P. Redfearn, “Multi-lead QRS detection using window pairs,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 3143–3146, 2012, doi: 10.1109/EMBC.2012.6346631.

S. Jain, M. K. Ahirwal, A. Kumar, V. Bajaj, and G. K. Singh, “QRS detection using adaptive filters: A comparative study,” ISA Trans., vol. 66, pp. 362–375, 2017, doi: 10.1016/j.isatra.2016.09.023.

A. K. Tanji, M. A. G. de Brito, M. G. Alves, R. C. Garcia, G. L. Chen, and N. R. N. Ama, “Improved noise cancelling algorithm for electrocardiogram based on moving average adaptive filter,” Electron., vol. 10, no. 19, pp. 1–18, 2021, doi: 10.3390/electronics10192366.

G. D. Fraser, A. D. C. Chan, J. R. Green, and D. MacIsaac, “Removal of electrocardiogram artifacts in surface electromyography using a moving average method,” MeMeA 2012 - 2012 IEEE Symp. Med. Meas. Appl. Proc., pp. 128–131, 2012, doi: 10.1109/MeMeA.2012.6226621.

X. Hu, Z. Xiao, and N. Zhang, “Removal of baseline wander from ECG signal based on a statistical weighted moving average filter,” J. Zhejiang Univ. Sci. C, vol. 12, no. 5, pp. 397–403, 2011, doi: 10.1631/jzus.C1010311.

Q. Xue, Y. H. Hu, and W. J. Tompkins, “Neural-Network-Based Adaptive Matched Filtering for QRS Detection,” IEEE Trans. Biomed. Eng., vol. 39, no. 4, pp. 317–329, 1992, doi: 10.1109/10.126604.

S. Sonali, O. Singh, and R. K. Sunkaria, “ECG signal denoising based on Empirical Mode Decomposition and moving average filter,” 2013 IEEE Int. Conf. Signal Process. Comput. Control. ISPCC 2013, no. i, pp. 1–6, 2013, doi: 10.1109/ISPCC.2013.6663412.

Y. W. Bai, W. Y. Chu, C. Y. Chen, Y. T. Lee, Y. C. Tsai, and C. H. Tsai, “The combination of Kaiser window and moving average for the low-pass filtering of the remote ECG signals,” Proc. IEEE Symp. Comput. Med. Syst., vol. 17, pp. 273–278, 2004, doi: 10.1109/cbms.2004.1311727.

L. Luu and A. Dinh, “Using Moving Average Method to Recognize Systole and Diastole on Seismocardiogram without ECG Signal,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, vol. 2018-July, pp. 3796–3799, 2018, doi: 10.1109/EMBC.2018.8513297.

M. M. Hassan, S. Huda, J. Yearwood, H. F. Jelinek, and A. Almogren, “Multistage fusion approaches based on a generative model and multivariate exponentially weighted moving average for diagnosis of cardiovascular autonomic nerve dysfunction,” Inf. Fusion, vol. 41, pp. 105–118, 2018, doi: 10.1016/j.inffus.2017.08.004.

M. Elgendi, M. Jonkman, and F. Deboer, “Frequency bands effects on QRS detection,” BIOSIGNALS 2010 - Proc. 3rd Int. Conf. Bio-inpsired Syst. Signal Process. Proc., pp. 428–431, 2010, doi: 10.5220/0002742704280431.

N. J. Domnik, S. Torbey, G. E. J. Seaborn, J. T. Fisher, S. G. Akl, and D. P. Redfearn, “Moving average and standard deviation thresholding (MAST): a novel algorithm for accurate R-wave detection in the murine electrocardiogram,” J. Comp. Physiol. B Biochem. Syst. Environ. Physiol., vol. 191, no. 6, pp. 1071–1083, 2021, doi: 10.1007/s00360-021-01389-3.

W. Thinking and K. Save, “IRIS-GMK Gareth Morgan KiwiSaver Scheme,” no. October, pp. 60–63, 2010.

H. Azami, K. Mohammadi, and B. Bozorgtabar, “An Improved Signal Segmentation Using Moving Average and Savitzky-Golay Filter,” J. Signal Inf. Process., vol. 03, no. 01, pp. 39–44, 2012, doi: 10.4236/jsip.2012.31006.

M. Elgendi, “Fast QRS Detection with an Optimized Knowledge-Based Method: Evaluation on 11 Standard ECG Databases,” PLoS One, vol. 8, no. 9, 2013, doi: 10.1371/journal.pone.0073557.

J. Malik, E. Z. Soliman, and H. T. Wu, “An adaptive QRS detection algorithm for ultra-long-term ECG recordings,” J. Electrocardiol., vol. 60, pp. 165–171, 2020, doi: 10.1016/j.jelectrocard.2020.02.016.

S. W. Chen, H. C. Chen, and H. L. Chan, “A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising,” Comput. Methods Programs Biomed., vol. 82, no. 3, pp. 187–195, 2006, doi: 10.1016/j.cmpb.2005.11.012.

H. Xiong, M. Liang, and J. Liu, “A Real-Time QRS Detection Algorithm Based on Energy Segmentation for Exercise Electrocardiogram,” Circuits, Syst. Signal Process., vol. 40, no. 10, pp. 4969–4985, 2021, doi: 10.1007/s00034-021-01702-z.

A. J. Khalaf and S. J. Mohammed, “A QRS-Detection Algorithm for Real-Time Applications,” Int. J. Intell. Eng. Syst., vol. 14, no. 1, pp. 356–367, 2020, doi: 10.22266/IJIES2021.0228.33.

G. M. Friesen, T. C. Jannett, S. L. Yates, S. R. Quint, and H. T. Nagle, “A Comparison of the Noise Sensitivity,” IEEE. Trans. Biomed. Eng., vol. 37, no. January, 1990.

J. Kim and H. Shin, “Simple and robust realtime QRS detection algorithm based on spatiotemporal characteristic of the QRS complex,” PLoS One, vol. 11, no. 3, pp. 1–13, 2016, doi: 10.1371/journal.pone.0150144.

I. I. Christov, “Real time electrocardiogram QRS detection using combined adaptive threshold,” Biomed. Eng. Online, vol. 3, pp. 1–9, 2004, doi: 10.1186/1475-925X-3-28.

M. Elgendi, B. Eskofier, S. Dokos, and D. Abbott, “Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems,” PLoS One, vol. 9, no. 1, 2014, doi: 10.1371/journal.pone.0084018.

S. N. Shivappriya, R. Shanthaselvakumari, and T. Gowrishankar, “ECG delineation using stationary wavelet transform,” Proc. - 2006 14th Int. Conf. Adv. Comput. Commun. ADCOM 2006, pp. 271–274, 2006, doi: 10.1109/ADCOM.2006.4289898.

M. A. Belkadi and A. Daamouche, “A robust QRS detection approach using stationary wavelet transform,” Multimed. Tools Appl., vol. 80, no. 15, pp. 22843–22864, 2021, doi: 10.1007/s11042-020-10500-9.

Published
2023-02-25
How to Cite
[1]
A. N. Berlianri Rizhky, I. D. G. Hari Wisana, and S. Das, “QRS Detection On Heart Rate Variability Readings using Two Moving Average Methods”, Indones.J.electronic.electromed.med.inf, vol. 5, no. 1, pp. 20-29, Feb. 2023.
Section
Research Article