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Vol. 9, No. 3, August 2024

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Android-Based Wireless Single-Lead Electrocardiogram: Heart Rate Measurement and ECG Signal Visualization

https://doi.org/10.22219/kinetik.v9i3.1943
Atika Novitasari
Universitas Sebelas Maret
Nuryani Nuryani
Universitas Sebelas Maret
Darsono Darsono
Universitas Sebelas Maret

Corresponding Author(s) : Nuryani Nuryani

nuryani@mipa.uns.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 9, No. 3, August 2024

Abstract

Heart rate (HR) is vital for medical and healthcare purposes. This study presents an Android-based heart rate measurement system utilizing a single-lead electrocardiogram (ECG). Three electrodes placed on the arm in lead I configuration capture the ECG signals. An AD8232 sensor amplifies the signal, which is then digitized by Arduino Nano and transmitted to an Android device via HC-05 Bluetooth. The Android application processes the ECG data using the Pan-Tompkins algorithm with an optimized threshold coefficient to extract HR information. The system displays the ECG waveform and the calculated HR on the user interface. Our evaluation demonstrates high accuracy with an error rate of only 0.042%, sensitivity of 99.84%, and positive predictive value of 97.06%. This research suggests the potential of this system for convenient and reliable HR monitoring using readily available smartphones.

Keywords

Electrocardiogram Pan-Tompkins Single Lead Android Heart Rate
Novitasari, A., Nuryani, N., & Darsono, D. (2024). Android-Based Wireless Single-Lead Electrocardiogram: Heart Rate Measurement and ECG Signal Visualization. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 9(3), 247-254. https://doi.org/10.22219/kinetik.v9i3.1943
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References
  1. Gilgen-Ammann, R., Schweizer, T., and Wyss, T., “RR Interval Signal Quality of A Heart Rate Monitor and An ECG Holter at Rest and During Exercise,” European Journal of Applied Physiology, Vol. 119, No. 7, Pp. 1525-1532, 2019. https://doi.org/10.1007/s00421-019-04142-5
  2. Marinho, L. B., Nascimento, N. de M. M., Souza, J. W. M., Gurgel, M. V., Reboucas Filho, P. P., and de Albuquerque, V. H. C., “A Novel Electrocardiogram Feature Extraction Approach for Cardiac Arrhythmia Classification,” Future Generation Computer System, Vol. 97, Pp. 564-577, 2019. https://doi.org/10.1016/j.future.2019.03.025
  3. Shaown, T., Hasan, I., Mim, M. M. R., and Hossain, M. S. H., “IoT-based Portable ECG Monitoring System for Smart Healthcare,” in 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Pp. 1-5, 2019. http://dx.doi.org/10.1109/ICASERT.2019.8934622
  4. Abdou, A. and Krishnan, S., “Horizons in Single-Lead ECG Analysis From Devices to Data”, Frontiers in Signal Processing, Vol. 2, Pp. 1-16, 2022. https://doi.org/10.3389/frsip.2022.866047
  5. Kranjec, J., Begus, S., Gersak, G., and Drnovsek, J., “Non-contact Heart Rate and Heart Rate Variability Measurements: A Review”, Biomedical Signal Processing and Control, Vol. 13, Pp. 102-112, 2014. https://doi.org/10.1016/j.bspc.2014.03.004
  6. Soe, Y. N. and Oo, K. K. K., "ECG Signal Classification using Discrete Wavelet Transform and Pan Tompkins Algorithm", International Journal Of Creative and Innovative Research In All Studies, Vol. 2, No. 11, Pp. 14-19, 2020.
  7. Liu, Y., Chen, J., Bao, N. Gupta, B. B., and Lv, Z., “Survei on Atrial Fibrillation Detection from A Single-lead ECG Wave for Internet of Medical Things,” Computer Communications, Vol. 178, Pp. 245-258, 2021. http://dx.doi.org/10.1016/j.comcom.2021.08.002
  8. Rana, A. and Kim, K. K., “Cardiac Disease Detection Using Modified Pan-Tompkins Algorithm,” Journal of Sensor Science and Technology, Vol. 28, No. 1, Pp. 13-16, 2019. http://dx.doi.org/10.5369/JSST.2019.28.1.13
  9. Utomo, T. P., Nuryani, N., and Darmanto, “QRS Peak Detection for Heart Rate Monitoring on Android Smartphone,” in Journal of Physics: Conference Series, Vol. 909, Pp. 1-7, 2017. http://dx.doi.org/10.1088/1742-6596/909/1/012006
  10. Shokoueinejad, M., Chiang, M., Lines, S., Wang, F., Tompkins, W., and Webster, J. G., “Systematic Design and HRV Analysis of a Portable ECG System Using Arduino and LabVIEW for Biomedical Engineering Training,” Internasional Journal of Electronics and Electrical Engineering, Vol. 5, No. 5, Pp. 301-311, 2017. http://dx.doi.org/10.18178/ijeee.5.5.301-311
  11. Utomo, T. P., Nuryani, N., and Nugroho, A. S., “Automatic QRS-complex Peak Detector Based on Moving Average and Thresholding,” in Journal of Physics: Conference Series, Vol. 1153, Pp. 1-6, 2019. http://dx.doi.org/10.1088/1742-6596/1153/1/012039
  12. Shalihah, A., Alhafid, F., Subekti, N. Y. S., Utomo, T.P., and Nuryani, N., “Electrocardiogram Monitoring System Based on Android Smartphone and Microcontroller Unit,” in Journal of Physics: Conference Series, Vol. 1153, Pp. 1-5, 2017.
  13. Abd Al-Jabbar, E. Y., Al-Hatab, M. M. M., Qasim, M. A., Fathel, W. R., and Fadhil, M. A., “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 , Pp. 172-184, 2023. https://dx.doi.org/10.54216/FPA.120214
  14. Liu, F., Wei, S., Li, Y., Jiang, X., Zhang, Z., Zhang, L., and Liu, C., “The Accuracy on the Common Pan-Tompkins Based QRS Detection Methods Through Low-Quality Electrocardiogram Database,” Journal of Medical Imaging and Health Informatics, Vol. 7, Pp. 1-5, 2017. http://dx.doi.org/10.1166/jmihi.2017.2134
  15. Fariha, M. A. Z, Ikeura, R., Hayakawa, S., and Tsutsumi, S., “Analysis of Pan-Tompkins Algorithm Performance with Noisy ECG Signals,” in Journal of Physics: Conference Series, Vol. 1532, Pp. 1-11, 2020. http://dx.doi.org/10.1088/1742-6596/1532/1/012022
  16. Vasudeva, S. T., Rao, S. S., Panambur, N. K., Shettigar, A. K., Mahabala, C., Chandrashekarappa, M. P. G., and Linul, E., “Development of A Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals”, Applied Sciences, Vol. 12, No. 15, Pp. 1-25, 2022. https://doi.org/10.3390/app12157711
  17. Jimenez-Serrano, S., Rodrigo, M., Calvo, C. J., Millet, J., and Castells, F., “From 12 to 1 ECG Lead: Multiple Cardiac Condition Detection Mixing A Hybrid Machine Learning Approach with A One-versus-rest Classification Strategy”, Physiological Measurement, Vol. 43, No. 6, Pp. 1-17, 2022. http://dx.doi.org/10.1088/1361-6579/ac72f5
  18. Shokoueinejad, M., Mehdi Shokoueinejad, M.C., Chiang, M., Lines, S., Wang, F., Tompkins, W.J., and Webster, J.G., “Systematic Design and HRV Analysis of a Portable ECG System Using Arduino and LabVIEW for Biomedical Engineering Training”, International Journal of Electronics and Electrical Engineering, Vol. 5, No. 5, Pp. 301-311, 2017. http://dx.doi.org/10.18178/ijeee.5.5.301-311
  19. Chhabra, M., and Kalsi, M., “Real Time ECG monitoring system based on Internet of Things (IoT)”, International Journal of scientific and research publications, Vol. 7, No. 8, Pp. 547-550, 2017.
  20. Hamad, A. M., dan Jasim, A. D., “Remote ECG Signal Monitoring and Classification Based on Arduino with AD8232 Sensor”, University of Thi-Qar Journal for Engineering Sciences, Vol. 11, No. 2, Pp. 95-101. http://dx.doi.org/10.31663/tqujes.11.2.393(2021)
  21. Analog Devices, “Single-lead Heart Rate Monitor Front End AD8232”, in Analog Devices, Pp. 1-32, 2013.
  22. Nguyen, L., Perry, R., Seneres, L., and Seyedmahmoud, N., “Analog Integrated Circuit Applications”, in Faculty of the Worcester Polytechnic Institute, Pp. 1-131, 2015.
  23. Tyagi, D. And Kumar R., “Identification of QRS Segments of Electrocardiogram Signals Using Feature Extraction”, in 6th International Conference for Convergence in Technology I2CT, Pp. 1-5, 2021. http://dx.doi.org/10.1109/I2CT51068.2021.9417869
  24. Pan, J., and Tompkins, W. J., “A Real-Time QRS Detection Algorithm,” IEEE Transactional Biomedical Engineering, Vol. BME-32, No. 3, Pp. 230-236, 1985. https://doi.org/10.1109/TBME.1985.325532
  25. Neri, L., Oberdier, M. T., Augello, A., Suzuki, M., Tumarkin, E., Jaipalli, S., Geminiani, G. A., Halperin, H. R., and Borghi C., “Algorithm for Mobile Platform-Based Real-Time QRS Detection,” Sensors, Vol. 23, No. 3., Pp. 1-9, 2023. http://dx.doi.org/10.3390/s23031625
  26. Apandi, Z. F. M., Ikeura, R., Hayakawa, S., and Tsutsumi, S., “QRS Detection in Electrocardiogram Signal of Exercise Physical Activity,” in Journal of Physics: Conferen ce Series, Vol. 2319, No. 1, Pp. 1-9, 2022. http://dx.doi.org/10.1088/1742-6596/2319/1/012021
  27. Urteaga, J., Elola, A., Aramendi, E., Norvik, E.,and Skogvoll, E., “Automated Algorithm for QRS Detection in Cardiac Arrest Patients with PEA,” Computing in Cardiology, Vol. 49, Pp. 3-6, 2022. https://doi.org/10.22489/CinC.2022.270
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