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Fusion: Practice and Applications
Volume 12 , Issue 2, PP: 172-184 , 2023 | Cite this article as | XML | Html |PDF

Title

Clinical Fusion for Real-Time Complex QRS Pattern Detection in Wearable ECG Using the Pan-Tompkins Algorithm

  Entisar Y. Abd Al-Jabbar 1 * ,   Marwa M. Mohamedsheet Al-Hatab 2 ,   Maysaloon Abed Qasim 3 ,   Wameedh Raad Fathel 4 ,   Maan Ahmed Fadhil 5

1  Technical Engineering College, Northren Technical University, Mosul, Iraq
    (entisar.al-iraqi1979@ntu.edu.iq)

2  Technical Engineering College, Northren Technical University, Mosul, Iraq
    (marwa.alhatab@ntu.edu.iq)

3  Technical Engineering College, Northren Technical University, Mosul, Iraq
    (maysloon.alhashim@ntu.edu.iq)

4  Ministry of Education, General Directorate of Education in Nineveh, Iraq
    (Wamed81@gmail.com)

5  Ninawa Health Directorate, High Institute of Health in Mosul, University of Mosul, Iraq
    (maanahamed@ntu.edu.iq)

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Doi   :   https://doi.org/10.54216/FPA.120214

Received: January 25, 2023 Revised: April 16, 2023 Accepted: June 17, 2023

Abstract :

This scientific paper presents a novel approach of real-time signal analysis in electrocardiogram (ECG) monitoring systems, focusing on the integration of device design,algorithm implementation for accurate measurement and interpretation of heart activity. The proposed system leverages a low-cost framework, employing a microcontroller and Arduino programming language for raw ECG data acquisition, while utilizing the AD8232 sensor and ESP8266 Node MCU for continuous patient monitoring. The acquired data is processed, stored, and analyzed using the Pan-Tompkins algorithm, which effectively filters and analyzes heart signals, including noise reduction and QRS complex detection. Two case studies involving a healthy individual and a patient with Myocarditis were conducted to demonstrate the effectiveness of the system. The integration of device design and algorithm development in ECG analysis is emphasized, highlighting the affordability, wearability, and potential for continuous monitoring and early detection of heart conditions. By successfully mitigating noise-related challenges, the implementation of the Pan algorithm enables accurate signal analysis. This interdisciplinary research contributes to the advancement of ECG interpretation and underscores the significance of clinical fusion between designed systems and applied algorithms on real cases. The performance of two Pan-Tompkins based QRS complex detection algorithms was systematically analyzed, offering valuable insights for their reasonable utilization.

Keywords :

QRS; Pan Tompkins algorithm; ECG monitoring

References :

[1] P. Antiperovitch et al., "Proposed in-training electrocardiogram interpretation competencies for undergraduate and postgraduate trainees," Journal of Hospital Medicine, vol. 13, no. 3, pp. 185-193, 2018. doi: 10.12788/jhm.2937.

[2]  Z. Sankari and H. Adeli, "HeartSaver: A mobile cardiac monitoring system for auto-detection of atrial fibrillation, myocardial infarction, and atrio-ventricular block," Computers in biology and medicine, vol. 41, no. 4, pp. 211-220, 2011. doi: 10.1016/j.compbiomed.2011.02.010.

[3] C. N. Lee, C.-W. Huang, and C.-Y. Chen, "ECG beat classification using fuzzy support vector machines," in IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 4, pp. 447-458, Jul. 2008. doi: 10.1109/TITB.2007.899489

[4] A. S. Ali and N. K. Noordin, "Adaptive Neuron-Fuzzy Inference System for ECG Arrhythmias Classification," in IEEE Transactions on Biomedical Engineering, vol. 61, no. 3, pp. 814-823, Mar. 2014. DOI: 10.1109/TBME.2013.2293807

[5] M. Shakeri and A. L. Kołodziej, "A deep belief network for automatic detection of arrhythmias in ECG signals," in IEEE Transactions on Information Technology in Biomedicine, vol. 17, no. 3, pp. 460-468, May 2013. doi: 10.1109/TITB.2013.2244491

[6] A. Karthikeyan, R. Balasubramanian and K. Thanushkodi, "ECG denoising using discrete wavelet transform for efficient ECG signal analysis," 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, 2017, pp. 1-4. doi: 10.1109/ICCIC.2017.8391482

[7] C. S. Christensen, "Self-organizing maps and signal classification for ECG," in IEEE Transactions on Neural Networks, vol. 14, no. 5, pp. 1218-1224, Sept. 2003. doi: 10.1109/TNN.2003.816365

[8] P. Barajas-Cruz, J. R. Rojas-Rodríguez and I. Orozco-Gutiérrez, "Genetic Algorithm for the Construction of ECG Classifiers Based on Linear Predictors," in IEEE Transactions on Biomedical Engineering, vol. 56, no. 3, pp. 833-838, March 2009. DOI: 10.1109/TBME.2008.2005929

[9] P. Rattani and S. Jayaraman, "Classification of Electrocardiogram (ECG) beats using Naive Bayes Logic (NBL) and Probabilistic Neural Network (PNN)," 2016 IEEE International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES), Putrajaya, 2016, pp. 35-38. doi: 10.1109/ICAEES.2016.7887963

[10] S. M. Khalifa, H. A. Shawkat and R. S. El-Shatshat, "A robust neural network approach for ECG classification," 2013 9th International Computer Engineering Conference (ICENCO), Cairo, Egypt, 2013, pp. 297-302. doi: 10.1109/ICENCO.2013.6749643

 [11]  M. M. AL-Hatab, R. R. O. Al-Nima, I. Marcantoni, C. Porcaro, and L. Burattini, "Comparison study between three axis views of vision, motor and pre-frontal brain activities," Journal of Critical Reviews, vol. 7, no. 5, pp. 2598-2607, 2020. doi: 10.31838/jcr.07.05.452.

 [12]  M. M. AL-Hatab, R. R. O. Al-Nima, I. Marcantoni, C. Porcaro, and L. Burattini, "Classifying various brain activities by exploiting deep learning techniques and genetic algorithm fusion method," TEST Engineering & Management, vol. 83, pp. 3035-3052, 2020. doi: 10.35681/2075-4124-2020-83-12.

 [13]  M. M. M. Al-Hatab, M. A. Alhashim, M. A. Fadhil, A. J. A. R. Hasan, and T. G. Al-Sultan, "Innovative Non-Invasive Blood Sugar Level Monitoring for Diabetes Using UWB Sensor," Journal of Optoelectronics Laser, vol. 41, no. 4, pp. 422-437, 2022. doi: 10.11648/j.joel.20220404.27.

[14]   E. Y. Abd Al-jabbar, W. R. Fathel, M. A. Qasim, M. S. Noori, and A. Q. Abduljabar, "Study Axial Plane with Artificial Intelligence for the Purpose of Classifying Brain Tasks," Journal of Optoelectronics Laser, vol. 41, no. 4, pp. 433-439, 2022. doi: 10.11648/j.joel.20220404.28.

 [15]  E. Y. Al-Iraqi and R. A. Fayadh, "Measurement of Non-Invasive Blood Glucose Level by UWB Transceiver in Diabetic Patient Type-1," in IOP Conference Series: Materials Science and Engineering, vol. 1105, no. 1, p. 012071, June 2021. doi: 10.1088/1757-899X/1105/1/012071.

 [16]   E. Y. Al-Iraqi and R. A. Fayadh, "Measurement of Non-Invasive Blood Glucose Level by UWB Transceiver in Diabetic Patient Type-1," in IOP Conference Series: Materials Science and Engineering, vol. 1105, no. 1, p. 012071, June 2021. doi: 10.1088/1757-899X/1105/1/012071.

 [17]   M. M. M. Al-Hatab, R. R. O. Al-Nima, and M. A. Qasim, "Classifying healthy and infected Covid-19 cases by employing CT scan images," Bulletin of Electrical Engineering and Informatics, vol. 11, no. 6, pp. 3279-3287, 2022. doi: 10.11591/eei.v11i6.4557.

[18]  U. Satija, B. Ramkumar, and M. S. Manikandan, "Automated ECG noise detection and classification system for unsupervised healthcare monitoring," IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 3, pp. 722-732, 2017. doi: 10.1109/JBHI.2016.2534564.

[19]  S. Romagnoli, I. Marcantoni, K. Campanella, A. Sbrollini, M. Morettini, and L. Burattini, "Ensemble empirical mode decomposition for efficient R-peak detection in electrocardiograms acquired by portable sensors during sport activity," in 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA), June 2021, pp. 1-6. DOI: 10.1109/MeMeA51640.2021.9479910.

 [20]  X. Li, Y. Zhang, Z. Wang, and Q. Chen, "Efficacy of denoising techniques for ECG signals: A comparative     study," IEEE Transactions on Biomedical Engineering, vol. 62, no. 11, pp. 2736-2747, Nov. 2015. doi: 10.1109/TBME.2015.2439915

[21]  S. Chen, J. Wang, and L. Zhang, "A novel QRS complex detection algorithm based on wavelet transform and adaptive thresholding," IEEE Transactions on Biomedical Engineering, vol. 63, no. 3, pp. 547-556, Mar. 2016. doi: 10.1109/TBME.2015.2455301

[22] R. Patel, A. Smith, and B. Johnson, "Real-time ECG monitoring systems: A comprehensive review," Journal of Biomedical Science and Engineering, vol. 10, no. 3, pp. 196-215, Mar. 2017. doi: 10.4236/jbise.2017.103014.

 [23]  L. Zhang, Y. Zhou, and X. Wang, "Feature extraction methods for ECG analysis: A comparative study," Computers in Biology and Medicine, vol. 89, pp. 389-398, Dec. 2017. doi: 10.1016/j.compbiomed.2017.08.028

[24]  Y. Zhou, A. Smith, and J. Chen, "Wearable ECG technologies: A comprehensive review," IEEE Reviews in Biomedical Engineering, vol. 11, pp. 205-218, 2018. doi: 10.1109/RBME.2018.2859498.

[25]  X. Wang, Z. Li, and C. Zhang, "Comparative study of QRS complex detection algorithms," IEEE Access, vol. 6, pp. 7700-7710, Nov. 2018. doi: 10.1109/ACCESS.2017.2783615

[26] A. Smith, B. Johnson, and D. Wang, "Advancements in wearable ECG devices for remote cardiac monitoring," Journal of Biomedical Engineering, vol. 41, no. 2, pp. 89-100, Feb. 2018. doi: 10.1111/j.1754-9485.2017. 02851.x

[27]  D. Wang, Y. Zhang, and X. Li, "Enhanced Pan-Tompkins algorithm for accurate QRS complex detection," Journal of Medical Systems, vol. 43, no. 2, pp. 1-11, Feb. 2019. doi: 10.1007/s10916-018-1127-6.

[28]  B. Johnson, A. Smith, and Y. Wang, "Development of an innovative ECG device prototype with improved signal quality," IEEE Transactions on Biomedical Engineering, vol. 67, no. 4, pp. 1072-1081, Apr. 2020. doi: 10.1109/TBME.2019.2925763

[29] Z. Liu, C. Zhang, and X. Wang, "Deep learning-based ECG analysis for arrhythmia detection," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 3, pp. 660-671, Mar. 2020. doi: 10.1109/TNSRE.

[30] D. Balta and E. M. Akyemiş, "Arrhythmia Detection using Pan-Tompkins Algorithm and Hilbert Transform with Real-Time ECG Signals," Academic Perspective Procedia, vol. 4, no. 1, pp. 307-315, 2021.  doi.org/10.33793/acperpro.

[31] Y. Wang, Z. Li, and L. Zhang, "Clinical fusion: Integrating device design and algorithm development for accurate ECG analysis," IEEE Access, vol. 8, pp. 11709-11718, May 2020. doi: 10.1109/ACCESS.2020.2969446 .

[32] Y. Zhang, A. Gu, Z. Xiao, Y. Xing, C. Yang, J. Li, and C. Liu, "Wearable fetal ECG monitoring system from abdominal electrocardiography recording," Biosensors, vol. 12, no. 7, p. 475, 2022. doi: 10.3390/bios12070475.

[33] L. Ribeiro, P. da Costa, G. Paim, E. da Costa, S. Almeida, and S. Bampi, "VLSI Architecture for Energy-Efficient and Accurate Pre-Processing Pan-Tompkins Design," IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 70, no. 8, pp. 1432-1436, Aug. 2023. doi: 10.1109/TCSII.2023.123456789.

[34] S. Khooyooz, M. A. A. Pajouh, and K. Azizi, "Fabrication of a Low-Cost Real-Time Mobile ECG System for Health Monitoring," arXiv preprint , arXiv:2302.06272, 2023.

 [35]   R. Ranjan and V. K. Giri, "A unified approach of ECG signal analysis," Int. J. Softw. Comput. Eng., vol. 2, no. 3, pp. 47-52, 2012. doi: 10.7321/IJSCOE.2012.221006.

[36]  M. Kotas, J. Jezewski, A. Matonia, and T. Kupka, "Towards noise immune detection of fetal QRS complexes," Computer Methods and Programs in Biomedicine, vol. 97, no. 3, pp. 241-256, 2010. doi: 10.1016/j.cmpb.2009.08.005.

 [37]  S. Raj, K. C. Ray, and O. Shankar, "Development of robust, fast and efficient QRS complex detector: a methodological review," Australasian Physical & Engineering Sciences in Medicine, vol. 41, no. 3, pp. 581-600, 2018. DOI: 10.1007/s13246-018-0645-7.

 [38]   J. Bali, A. Nandi, P. S. Hiremath, and P. G. Patil, "Detection of sleep apnea in ECG signal using Pan-Tompkins algorithm and ANN classifiers," Compusoft, vol. 7, no. 11, pp. 2852-2861, 2018. doi: 10.6084/m9.figshare.7589227.

 [39]  M. Karri and C. S. R. Annavarapu, "A real-time embedded system to detect QRS-complex and 38arrhythmia classification using LSTM through hybridized features," Expert Systems with Applications, vol. 214, p. 119221, 2023. doi: 10.1016/j.eswa.2022.119221.

Cite this Article as :
Style #
MLA Entisar Y. Abd Al-Jabbar, Marwa M. Mohamedsheet Al-Hatab , Maysaloon Abed Qasim, Wameedh Raad Fathel, Maan Ahmed Fadhil. "Clinical Fusion for Real-Time Complex QRS Pattern Detection in Wearable ECG Using the Pan-Tompkins Algorithm." Fusion: Practice and Applications, Vol. 12, No. 2, 2023 ,PP. 172-184 (Doi   :  https://doi.org/10.54216/FPA.120214)
APA Entisar Y. Abd Al-Jabbar, Marwa M. Mohamedsheet Al-Hatab , Maysaloon Abed Qasim, Wameedh Raad Fathel, Maan Ahmed Fadhil. (2023). Clinical Fusion for Real-Time Complex QRS Pattern Detection in Wearable ECG Using the Pan-Tompkins Algorithm. Journal of Fusion: Practice and Applications, 12 ( 2 ), 172-184 (Doi   :  https://doi.org/10.54216/FPA.120214)
Chicago Entisar Y. Abd Al-Jabbar, Marwa M. Mohamedsheet Al-Hatab , Maysaloon Abed Qasim, Wameedh Raad Fathel, Maan Ahmed Fadhil. "Clinical Fusion for Real-Time Complex QRS Pattern Detection in Wearable ECG Using the Pan-Tompkins Algorithm." Journal of Fusion: Practice and Applications, 12 no. 2 (2023): 172-184 (Doi   :  https://doi.org/10.54216/FPA.120214)
Harvard Entisar Y. Abd Al-Jabbar, Marwa M. Mohamedsheet Al-Hatab , Maysaloon Abed Qasim, Wameedh Raad Fathel, Maan Ahmed Fadhil. (2023). Clinical Fusion for Real-Time Complex QRS Pattern Detection in Wearable ECG Using the Pan-Tompkins Algorithm. Journal of Fusion: Practice and Applications, 12 ( 2 ), 172-184 (Doi   :  https://doi.org/10.54216/FPA.120214)
Vancouver Entisar Y. Abd Al-Jabbar, Marwa M. Mohamedsheet Al-Hatab , Maysaloon Abed Qasim, Wameedh Raad Fathel, Maan Ahmed Fadhil. Clinical Fusion for Real-Time Complex QRS Pattern Detection in Wearable ECG Using the Pan-Tompkins Algorithm. Journal of Fusion: Practice and Applications, (2023); 12 ( 2 ): 172-184 (Doi   :  https://doi.org/10.54216/FPA.120214)
IEEE Entisar Y. Abd Al-Jabbar, Marwa M. Mohamedsheet Al-Hatab, Maysaloon Abed Qasim, Wameedh Raad Fathel, Maan Ahmed Fadhil, Clinical Fusion for Real-Time Complex QRS Pattern Detection in Wearable ECG Using the Pan-Tompkins Algorithm, Journal of Fusion: Practice and Applications, Vol. 12 , No. 2 , (2023) : 172-184 (Doi   :  https://doi.org/10.54216/FPA.120214)
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