R PEAK DETERMINATION USING A WDFR ALGORITHM AND ADAPTIVE THRESHOLD

Thanh-Nghia NGUYEN

nghiant@hcmute.edu.vn
Department of Industrial Electronics and Biomedical Engineering, HCMC University of Technology and Education, Ho Chi Minh (Viet Nam)

Thanh-Hai NGUYEN


Department of Industrial Electronics and Biomedical Engineering, HCMC University of Technology and Education, Ho Chi Minh, (Viet Nam)

Ba-Viet NGO


Department of Industrial Electronics and Biomedical Engineering, HCMC University of Technology and Education, Ho Chi Minh (Viet Nam)

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

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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   Google Scholar

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Published
2022-09-30

Cited by

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

Authors

Thanh-Nghia NGUYEN 
nghiant@hcmute.edu.vn
Department of Industrial Electronics and Biomedical Engineering, HCMC University of Technology and Education, Ho Chi Minh Viet Nam

Authors

Thanh-Hai NGUYEN 

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

Authors

Ba-Viet NGO 

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

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