R PEAK DETERMINATION USING A WDFR ALGORITHM AND ADAPTIVE THRESHOLD
Thanh-Nghia NGUYEN
nghiant@hcmute.edu.vnDepartment 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 thresholdReferences
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
Authors
Thanh-Nghia NGUYENnghiant@hcmute.edu.vn
Department of Industrial Electronics and Biomedical Engineering, HCMC University of Technology and Education, Ho Chi Minh Viet Nam
Authors
Thanh-Hai NGUYENDepartment of Industrial Electronics and Biomedical Engineering, HCMC University of Technology and Education, Ho Chi Minh, Viet Nam
Authors
Ba-Viet NGODepartment of Industrial Electronics and Biomedical Engineering, HCMC University of Technology and Education, Ho Chi Minh Viet Nam
Statistics
Abstract views: 249PDF downloads: 102
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.
Similar Articles
- Olutayo BOYINBODE, Paul OLOTU, Kolawole AKINTOLA, DEVELOPMENT OF AN ONTOLOGY-BASED ADAPTIVE PERSONALIZED E-LEARNING SYSTEM , Applied Computer Science: Vol. 16 No. 4 (2020)
- Tomasz NOWICKI, Adam GREGOSIEWICZ, Zbigniew ŁAGODOWSKI, PRODUCTIVITY OF A LOW-BUDGET COMPUTER CLUSTER APPLIED TO OVERCOME THE N-BODY PROBLEM , Applied Computer Science: Vol. 17 No. 4 (2021)
- Muaayed F. AL-RAWI, CONVENTIONAL ENERGY EFFICIENT ROUTING PROTOCOLS IN WIRELESS SENSOR NETWORKS , Applied Computer Science: Vol. 16 No. 3 (2020)
- Lucian LUPŞA-TĂTARU, NOVEL TECHNIQUE OF CUSTOMIZING THE AUDIO FADE-OUT SHAPE , Applied Computer Science: Vol. 14 No. 3 (2018)
- Łukasz WOJCIECHOWSKI, Tadeusz CISOWSKI, MODEL OF A COMPUTER SYSTEM FOR SELECTION OF OPERATING PARAMETERS FOR TRANSPORT VEHICLES IN THE ASPECT OF THEIR DURABILITY , Applied Computer Science: Vol. 14 No. 4 (2018)
- Jacek CABAN, Grzegorz LITAK, Bartłomiej AMBROŻKIEWICZ, Leszek GARDYŃSKI, Paweł STĄCZEK, Piotr WOLSZCZAK, IMPACT-BASED PIEZOELECTRIC ENERGY HARVESTING SYSTEM EXCITED FROM DIESEL ENGINE SUSPENSION , Applied Computer Science: Vol. 16 No. 3 (2020)
- Robert KARPIŃSKI, Anna MACHROWSKA, Marcin MACIEJEWSKI, APPLICATION OF ACOUSTIC SIGNAL PROCESSING METHODS IN DETECTING DIFFERENCES BETWEEN OPEN AND CLOSED KINEMATIC CHAIN MOVEMENT FOR THE KNEE JOINT , Applied Computer Science: Vol. 15 No. 1 (2019)
- Rosa Maria VAZQUEZ, Edmundo BONILLA, Eduardo SANCHEZ, Oscar ATRIANO, Cinthya BERRUECOS, APPLICATION OF DATA MINING TECHNIQUES TO FIND RELATIONSHIPS BETWEEN THE DISHES OFFERED BY A RESTAURANT FOR THE ELABORATION OF COMBOS BASED ON THE PREFERENCES OF THE DINERS , Applied Computer Science: Vol. 15 No. 2 (2019)
- Lucian LUPŞA-TĂTARU, IMPLEMENTING THE FADE-IN AUDIO EFFECT FOR REAL-TIME COMPUTING , Applied Computer Science: Vol. 15 No. 2 (2019)
- Anusha NALLAPAREDDY, DETECTION AND CLASSIFICATION OF VEGETATION AREAS FROM RED AND NEAR INFRARED BANDS OF LANDSAT-8 OPTICAL SATELLITE IMAGE , Applied Computer Science: Vol. 18 No. 1 (2022)
You may also start an advanced similarity search for this article.
