QRS Complex Detection On Heart Rate Variability Reading Using Discrete Wavelet Transform
Abstract
Heart Rate Variability or heart rate in humans is used to monitor the heart rate in humans, the function of the heart rate monitor is to monitor the human heart rate. The purpose of making this tool is to compare the results of heart rate readings using the discrete wavelet transform method to facilitate the detection of R peak. This can be learned by evaluating and studying each decomposition result from level 1 to level 4 on Discrete Wavelet Transform processing using Haar mother wavelets. This study uses a raspberry pi 3B as a microcontroller as a data processor that is obtained from the ECG module. From this study, it can be concluded that in heart rate readings, level 2 decomposition details coefficient has the best value as data processing that helps for heart rate readings with an error value of 0.531%, HRV readings of 0.005 in comparison with phantom tools and a standard deviation of 0.039. The advantage of this tool is a good precision value in HRV and BPM readings. In reading the HRV of the respondent, it was found that each initial condition of the patient's HRV would be high due to the respondent's unstable condition. The disadvantage of this tool is that there is a delay in running the program, there is no display in the form of a signal in real time.
Downloads
References
N. A. Manlong, J. Rahul, and M. Sora, “ST Segment Analysis for Early Detection of Myocardial Infarction,” Int. J. Comput. Sci. Eng., vol. 6, no. 6, pp. 1500–1504, 2018, doi: 10.26438/ijcse/v6i6.15001504.
J. W. Hurst, “Naming of the waves in the ECG, with a brief account of their genesis,” Circulation, vol. 98, no. 18, pp. 1937–1942, 1998, doi: 10.1161/01.CIR.98.18.1937.
S. Setiawidayat, J. T. Elektro, and U. Widyagama, “Penentuan Posisi Awal Dan Akhir Gelombang Ecg Tiap,” no. Ciastech, pp. 589–596, 2020.
P. K. Stein and Y. Pu, “Heart rate variability, sleep and sleep disorders,” Sleep Med. Rev., vol. 16, no. 1, pp. 47–66, 2012, doi: 10.1016/j.smrv.2011.02.005.
B. Xhyheri, O. Manfrini, M. Mazzolini, C. Pizzi, and R. Bugiardini, “Heart Rate Variability Today,” Prog. Cardiovasc. Dis., vol. 55, no. 3, pp. 321–331, 2012, doi: 10.1016/j.pcad.2012.09.001.
S. Laborde, E. Mosley, and J. F. Thayer, “Heart rate variability and cardiac vagal tone in psychophysiological research - Recommendations for experiment planning, data analysis, and data reporting,” Front. Psychol., vol. 8, no. FEB, pp. 1–18, 2017, doi: 10.3389/fpsyg.2017.00213.
D. Keenan, “Detection and correction of ectopic beats for HRV analysis applying discrete wavelet transforms,” Int. J. Inf. Technol, vol. 2, no. 10, pp. 338–344, 2005, [Online]. Available: http://www.waset.org/publications/10138.
K. D. Desai and M. S. Sankhe, “A real-time fetal ECG feature extraction using multiscale discrete wavelet transform,” 2012 5th Int. Conf. Biomed. Eng. Informatics, BMEI 2012, no. Bmei, pp. 407–412, 2012, doi: 10.1109/BMEI.2012.6512966.
I. H. Bruun, S. M. S. Hissabu, E. S. Poulsen, and S. Puthusserypady, “Automatic Atrial Fibrillation detection: A novel approach using discrete wavelet transform and heart rate variability,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 3981–3984, 2017, doi: 10.1109/EMBC.2017.8037728.
I. Nouira, A. Ben Abdallah, M. H. Bedoui, and M. Dogui, “A robust R peak detection algorithm using wavelet transform for heart rate variability studies,” Int. J. Electr. Eng. Informatics, vol. 5, no. 3, pp. 270–284, 2013, doi: 10.15676/ijeei.2013.5.3.3.
K. S. Basavaraju, C. M. Vikram, and C. Kishore, “DWT based SVM multi classifier approach for HR signal classification,” Proc. - 2014 4th Int. Conf. Adv. Comput. Commun. ICACC 2014, pp. 69–72, 2014, doi: 10.1109/ICACC.2014.22.
G. Jaswal, R. Parmar, and A. Kaul, “QRS Detection Using Wavelet Transform,” Int. J. …, vol. 1, no. 6, pp. 1–5, 2012, [Online]. Available: http://core.kmi.open.ac.uk/download/pdf/9331213.pdf.
R. Haddadi, E. Abdelmounim, M. El Hanine, and A. Belaguid, “Discrete wavelet transform based algorithm for recognition of QRS complexes,” Int. Conf. Multimed. Comput. Syst. -Proceedings, pp. 375–379, 2014, doi: 10.1109/ICMCS.2014.6911261.
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.
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.
W. Jenkal, R. Latif, A. Toumanari, A. Dliou, and O. El B’Charri, “Enhanced algorithm for QRS detection using discrete wavelet transform (DWT),” Proc. Int. Conf. Microelectron. ICM, vol. 2016-March, pp. 39–42, 2016, doi: 10.1109/ICM.2015.7437982.
M. Ryan Fajar Nurdin, S. Hadiyoso, and A. Rizal, “A low-cost Internet of Things (IoT) system for multi-patient ECG’s monitoring,” ICCEREC 2016 - Int. Conf. Control. Electron. Renew. Energy, Commun. 2016, Conf. Proc., pp. 7–11, 2017, doi: 10.1109/ICCEREC.2016.7814958.
J. Rahul, M. Sora, and L. Sharma, “Baseline correction of ECG using regression estimation method,” Proc. - 2019 4th Int. Conf. Internet Things Smart Innov. Usages, IoT-SIU 2019, pp. 1–5, 2019, doi: 10.1109/IoT-SIU.2019.8777622.
C. C. Chiu, C. M. Chuang, and C. Y. Hsu, “A novel personal identity verification approach using a discrete wavelet transform of the ECG signal,” Proc. - 2008 Int. Conf. Multimed. Ubiquitous Eng. MUE 2008, pp. 201–206, 2008, doi: 10.1109/MUE.2008.67.
S. Toinga, C. Carabali, and L. Ortega, “Development of a didactic platform for teaching the Einthoven’s Triangle,” 2017 IEEE 2nd Ecuador Tech. Chapters Meet. ETCM 2017, vol. 2017-Janua, pp. 1–6, 2018, doi: 10.1109/ETCM.2017.8247542.
V. Vijendra and M. Kulkarni, “ECG signal filtering using DWT haar wavelets coefficient techniques,” 1st Int. Conf. Emerg. Trends Eng. Technol. Sci. ICETETS 2016 - Proc., no. 1, 2016, doi: 10.1109/ICETETS.2016.7603040.
E. Erçelebi, “Electrocardiogram signals de-noising using lifting-based discrete wavelet transform,” Comput. Biol. Med., vol. 34, no. 6, pp. 479–493, 2004, doi: 10.1016/S0010-4825(03)00090-8.
Copyright (c) 2022 Arga Wihantara, I Dewa Gede Hariwisana, Andjar Pudji, Sari Luthfiyah, Vijay Anant Athavale
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
