Abstract
An electrocardiogram (ECG) system is a system used to analyze signals from the human heart, determine the health status of the heart, or diagnose diseases. Among the ECG signals measured using this system, we would like to propose a Pan-Tomkins algorithm that focuses on P wave and detecting P wave. To compare the performance of the developed algorithm, five actual measurement data of the electrocardiogram device were compared with Holter, and the error rate was 20%. If this is used, it is expected that more patients’ cardiovascular diseases can be prevented through wearables. The accuracy was 0.912568, the precision was 0.947368, and the sensitivity was 0.72 in the analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Randazzo, V., Ferretti, J., Pasero, E.: ECG WATCH: a real time wireless wearable ECG. In 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6. IEEE (2019)
Wasimuddin, M., Elleithy, K., Abuzneid, A.-S., Faezipour, M., Abuzaghleh, O.: Stages-based ECG signal analysis from traditional signal processing to machine learning approaches: a survey. IEEE Access 8, 177782–177803 (2020)
Pingale, S.L.: Using Pan Tompkin ‘S Method, ECG signal processing and dignose various diseases in Matlab. In: Proceedings of IRF International Conference, pp. 57–61 (2014)
Jindal, B., Devi, R.: MATLAB based GUI for ECG arrhythmia detection using Pan-Tompkin algorithm. In Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 754–759. IEEE (2018)
Talatov, Y., Mgrupov, T.: Algorithmic and software analysis and processing of ECG signals. In International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), pp. 0403–0406. IEEE (2019)
Narayana, K.V.L., Rao, A.B.: Wavelet based QRS detection in ECG using MATLAB. Innov. Syst. Des. Eng. 2(7), 60–69 (2011)
Wang, N., Zhou, J., Dai, G., Huang, J., Xie, Y.: Energy-efficient intelligent ECG monitoring for wearable devices. IEEE Trans. Biomed. Circuits Syst. 13(5), 1112–1121 (2019)
Rekik, S., Ellouze, N.: P-wave detection combining entropic criterion and wavelet transform. J. Signal Inf. Process. 6(03), 217 (2015)
Acknowledgements
This research is financial supported by “The digital pathology-based AI analysis solution development project” through the Ministry of Health and Welfare, Republic of Korea (HI21C0977) and supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2022R1I1A3072785).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jung, Y.W., Lee, JH. (2024). The Novel Electrocardiograph Sensor and Algorithm for Arrhythmia Computer Aided Detection. In: Choi, B.J., Singh, D., Tiwary, U.S., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2023. Lecture Notes in Computer Science, vol 14532. Springer, Cham. https://doi.org/10.1007/978-3-031-53830-8_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-53830-8_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53829-2
Online ISBN: 978-3-031-53830-8
eBook Packages: Computer ScienceComputer Science (R0)