R PEAK DETERMINATION USING A WDFR ALGORITHM AND ADAPTIVE THRESHOLD

Thanh-Nghia  NGUYEN; Thanh-Hai  NGUYEN; Ba-Viet  NGO

doi:10.35784/acs-2022-18

R PEAK DETERMINATION USING A WDFR ALGORITHM AND ADAPTIVE THRESHOLD

Article Sidebar

Open full text

pdf

Published: Sep 30, 2022

DOI: https://doi.org/10.35784/acs-2022-18

Main Article Content

DOI

https://doi.org/10.35784/acs-2022-18

Authors

Thanh-Nghia NGUYEN

nghiant@hcmute.edu.vn

Department of Industrial Electronics and Biomedical Engineering, HCMC University of Technology and Education, Ho Chi Minh

Thanh-Hai NGUYEN

nthai@hcmute.edu.vn

Department of Industrial Electronics and Biomedical Engineering, HCMC University of Technology and Education, Ho Chi Minh,

Ba-Viet NGO

vietnb@hcmute.edu.vn

Department of Industrial Electronics and Biomedical Engineering, HCMC University of Technology and Education, Ho Chi Minh

Abstract

The determination of the R peak position in the ECG signal helps physicians not only to know the heart rate per minute, but also to monitor the patient’s health related to heart disease. This paper proposes a system to accurately determine the R peak position in the ECG signal. The system consists of a pre-processing block for filtering out noise using a WDFR algorithm and highlighting the amplitude of the R peak and a threshold value is calculated for determining the R peak. In this research, the MIT-BIH ECG dataset with 48 records are used for evaluation of the system. The results of the SEN, +P, DER and ACC parameters related to the system quality are 99.70%, 99.59%, 0.70% and 99.31%, respectively. The obtained performance of the proposed R peak position determination system is very high and can be applied to determine the R peak of the ECG signal measuring devices in practice.

Keywords:

ECG signal, Wavelet transforms, WDFR algorithm, R peak determination, Adaptive threshold

References

Al, Z. M. A., Thapa, K., & Yang, S.-H. (2021). Improving R Peak Detection in ECG Signal Using Dynamic Mode Selected Energy and Adaptive Window Sizing Algorithm with Decision Tree Algorithm. Sensors, 21(19), 6682–6699. https://doi.org/10.3390/s21196682 DOI: https://doi.org/10.3390/s21196682

Alhussainy, A. M. H., & Jasim, A. D. (2021). Training feedforward neural network using genetic algorithm to diagnose left ventricular hypertrophy. TELKOMNIKA Telecommunication, Computing, Electronics and Control, 18(3), 1285–1291. https://doi.org/10.12928/TELKOMNIKA.v18i3.15225 DOI: https://doi.org/10.12928/telkomnika.v18i3.15225

Aziz, S., Ahmed, S., & Alouini, M.-S. (2021). ECG-based machine-learning algorithms for heartbeat classification. Scientific Reports, 11(1), 18738-18752. https://doi.org/10.1038/s41598-021-97118-5 DOI: https://doi.org/10.1038/s41598-021-97118-5

Cai, W., & Hu, D. (2020). QRS Complex Detection Using Novel Deep Learning Neural Networks. IEEE Access, 8, 97082–97089. https://doi.org/10.1109/ACCESS.2020.2997473 DOI: https://doi.org/10.1109/ACCESS.2020.2997473

Chen, A., Zhang, Y., Zhang, M., Liu, W., Chang, S., Wang, H., He, J., & Huang, Q. (2020). A Real Time QRS Detection Algorithm Based on ET and PD Controlled Threshold Strategy. Sensors (Basel), 20(14), 4003–4018. https://doi.org/10.3390/s20144003 DOI: https://doi.org/10.3390/s20144003

Chen, L., Yu, H., Huang, Y., & Jin, H. (2021). ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure. J Healthc Eng, 2021, 5802722–5802730. https://doi.org/10.1155/2021/5802722 DOI: https://doi.org/10.1155/2021/5802722

Dang, H., Sun, M., Zhang, G., Qi, X., Zhou, X., & Chang, Q. (2019). A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals. IEEE Access, 7, 75577–75590. https://doi.org/10.1109/ACCESS.2019.2918792 DOI: https://doi.org/10.1109/ACCESS.2019.2918792

Darmawahyuni, A., Nurmaini, S., Rachmatullah, M. N., Firdaus, F., & Tutuko, B. (2021). Unidirectional-bidirectional recurrent networks for cardiac disorders classification. TELKOMNIKA Telecommunication, Computing, Electronics and Control, 19(3), 902–910. https://doi.org/10.12928/TELKOMNIKA.v19i3.18876 DOI: https://doi.org/10.12928/telkomnika.v19i3.18876

Jang, J., Park, S., Kim, J.-K., An, J., & Jung, S. (2022). CNN-based Two Step R Peak Detection Method: Combining Segmentation and Regression. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1910–1914). IEEE. DOI: https://doi.org/10.1109/EMBC48229.2022.9871227

Kumar, A., Kumar, R., & Pandey, R. K. (2012). ECG Signal Compression using Optimum Wavelet Filter Bank Based on Kaiser Window. Procedia Engineering, 38(1), 2889–2902. https://doi.org/https://doi.org/10.1016/j.proeng.2012.06.338 DOI: https://doi.org/10.1016/j.proeng.2012.06.338

Laitala, J., Jiang, M., Syrjälä, E., Naeini, E. K., Airola, A., Rahmani, A. M., Dutt, N. K. & Liljeberg, P. (2020). Robust ECG R-peak detection using LSTM. In SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing (pp. 1104–1111). ACM Digital Library. DOI: https://doi.org/10.1145/3341105.3373945

Lee, M., Park, D., Dong, S.-Y., & Youn, I. (2018). A Novel R Peak Detection Method for Mobile Environments. IEEE Access, 6, 51227–51237. https://doi.org/10.1109/ACCESS.2018.2867329 DOI: https://doi.org/10.1109/ACCESS.2018.2867329

Lin, C.-C., Chang, H.-Y., Huang, Y.-H., & Yeh, C.-Y. (2019). A Novel Wavelet-Based Algorithm for Detection of QRS Complex. Applied Sciences, 9(10), 2142–2161. https://doi.org/10.3390/app9102142 DOI: https://doi.org/10.3390/app9102142

Lu, X., Pan, M., & Yu, Y. (2018). QRS Detection Based on Improved Adaptive Threshold. J Healthc Eng, 2018, 5694595–5694604. https://doi.org/10.1155/2018/5694595 DOI: https://doi.org/10.1155/2018/5694595

Meqdad, M. N., Abdali-Mohammadi, F., & Kadry, S. (2022). Meta Structural Learning Algorithm with Interpretable Convolutional Neural Networks for Arrhythmia Detection of Multi-Session ECG. IEEE Access, 10, 61410–61425. https://doi.org/10.1109/ACCESS.2022.3181727 DOI: https://doi.org/10.1109/ACCESS.2022.3181727

Mohebbanaaz, M., Sai, Y. P., & Kumari, L. V. R. (2021). Detection of cardiac arrhythmia using deep CNN and optimized SVM. Indonesian Journal of Electrical Engineering and Computer Science, 24(2), 217–225. https://doi.org/10.11591/ijeecs.v24.i1 DOI: https://doi.org/10.11591/ijeecs.v24.i1.pp217-225

Moody, G. B., & Mark, R. G. (2001). The impact of the MIT-BIH Arrhythmia Database. IEEE Engineering in Medicine and Biology Magazine, 20(3), 45–50. https://doi.org/10.1109/51.932724 DOI: https://doi.org/10.1109/51.932724

Nguyen, T.-N., Nguyen, T.-H., & Ngo, V.-T. (2020). Artifact elimination in ECG signal using wavelet transform. TELKOMNIKA Telecommunication, Computing, Electronics and Control, 18(2), 936–944. https://doi.org/10.12928/telkomnika.v18i2.14403 DOI: https://doi.org/10.12928/telkomnika.v18i2.14403

Nguyen, T.-N., & Nguyen, T.-H. (2021). Deep Learning Framework with ECG Feature-Based Kernels for Heart Disease Classification. Elektronika ir Elektrotechnika, 27(1), 48–59. https://doi.org/10.5755/j02.eie.27642 DOI: https://doi.org/10.5755/j02.eie.27642

Nguyen, T., Qin, X., Dinh, A., & Bui, F. (2019). Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter. Sensors, 19(18), 3997–4014. https://doi.org/10.3390/s19183997 DOI: https://doi.org/10.3390/s19183997

Olanrewaju, R. F., Ibrahim, S. N., Asnawi, A. L., & Altaf, H. (2021). Classification of ECG signals for detection of arrhythmia and congestive heart failure based on continuous wavelet transform and deep neural networks. Indonesian Journal of Electrical Engineering and Computer Science, 22(3), 1520–1528. https://doi.org/10.11591/ijeecs.v22.i3.pp1520-1528 DOI: https://doi.org/10.11591/ijeecs.v22.i3.pp1520-1528

Park, J.-S., Lee, S.-W., & Park, U. (2017). R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope. J Healthc Eng, 2017, 4901017–4901032. https://doi.org/10.1155/2017/4901017 DOI: https://doi.org/10.1155/2017/4901017

Qin, Q., Li, J., Yue, Y., & Liu, C. (2017). An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm. J Healthc Eng, 2017, 1–14. https://doi.org/10.1155/2017/5980541 DOI: https://doi.org/10.1155/2017/5980541

Rahman, N. A. A., & Jambek, A. B. (2019). Biomedical health monitoring system design and analysis. Indonesian Journal of Electrical Engineering and Computer Science, 13(3), 1056–1064. https://doi.org/10.11591/ijeecs.v13.i3.pp1056-1064 DOI: https://doi.org/10.11591/ijeecs.v13.i3.pp1056-1064

Ribeiro, A. H., Ribeiro, M. H., Paixão, G. M. M., Oliveira, D. M., Gomes, P. R., Canazart, J. A., Ferreira, M. P. S., Andersson, C. R., Macfarlane, P. W., Meira, W. Jr., Schön, T. B., & Ribeiro, A. L. P. (2020). Automatic diagnosis of the 12-lead ECG using a deep neural network. Nature Communications, 11(1), 1760–1769. https://doi.org/10.1038/s41467-020-15432-4 DOI: https://doi.org/10.1038/s41467-020-15432-4

Sharma, L. D., & Sunkaria, R. K. (2016). A robust QRS detection using novel pre-processing techniques and kurtosis based enhanced efficiency. Measurement, 87, 194–204. https://doi.org/10.1016/j.measurement.2016.03.015 DOI: https://doi.org/10.1016/j.measurement.2016.03.015

Suboh, M. Z., Jaafar, R., Nayan, N. A., & Harun, N. H. (2020). Shannon Energy Application for Detection of ECG R-peak using Bandpass Filter and Stockwell Transform Methods. Advances in Electrical and Computer Engineering, 20(3), 41–48. https://doi.org/10.4316/AECE.2020.03005 DOI: https://doi.org/10.4316/AECE.2020.03005

Wu, M., Lu, Y., Yang, W., & Wong, S. Y. (2021). A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network [Original Research]. Frontiers in Computational Neuroscience, 14, 1–10. ttps://doi.org/10.3389/fncom.2020.564015 DOI: https://doi.org/10.3389/fncom.2020.564015

Xiang, Y., Lin, Z., & Meng, J. (2018). Automatic QRS complex detection using two-level convolutional neural network. BioMedical Engineering OnLine, 17(1), 13. https://doi.org/10.1186/s12938-018-0441-4 DOI: https://doi.org/10.1186/s12938-018-0441-4

Zahid, M. U., Kiranyaz, S., Ince, T., Devecioglu, O. C., Chowdhury, M. E. H., Khandakar, A., Tahir, A., & Gabbouj, M. (2021). Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network. IEEE Transactions on Biomedical Engineering, 69(1), 119–128. https://doi.org/10.1109/TBME.2021.3088218 DOI: https://doi.org/10.1109/TBME.2021.3088218

Zalabarria, U., Irigoyen, E., Martinez, R., & Lowe, A. (2020). Online robust R-peaks detection in noisy electrocardiograms using a novel iterative smart processing algorithm. Applied Mathematics and Computation, 369, 124839–124852. https://doi.org/10.1016/j.amc.2019.124839 DOI: https://doi.org/10.1016/j.amc.2019.124839

Zhang, Z., Li, Z., & Li, Z. (2020). An Improved Real-Time R-Wave Detection Efficient Algorithm in Exercise ECG Signal Analysis. J Healthc Eng, 2020, 8868685. https://doi.org/10.1155/2020/8868685 DOI: https://doi.org/10.1155/2020/8868685

Zhou, P., Schwerin, B., Lauder, B., & So, S. (2020). Deep Learning for Real-time ECG R-peak Prediction. 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS). IEEE. DOI: https://doi.org/10.1109/ICSPCS50536.2020.9310052

Article Details

NGUYEN, T.-N. ., NGUYEN, T.-H. ., & NGO, B.-V. . (2022). R PEAK DETERMINATION USING A WDFR ALGORITHM AND ADAPTIVE THRESHOLD. Applied Computer Science, 18(3), 19–30. https://doi.org/10.35784/acs-2022-18

ACM
ACS
APA
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver

Endnote/Zotero/Mendeley (RIS)
BibTeX

Abstract views: 334

License

All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.