Abstract
Electrocardiogram (ECG) is a graph that depicts blood circulation through the heart. ECG is also used for depicting the state of health of an individual and is helpful in disease diagnosis. The target of this work is to check the application of curve fitting on ECG signals based on the Fourier series analysis method. When ECG signals are approximated by the Fourier series model, the fitting for the cardiac cycle is used for judging arrhythmias. The data used here was sourced from the MIT-BIH arrhythmia database, and only ECG recordings were utilized for the purpose of this study. The study has presented efficient methods for signal identification with the help of fitting parameters and ECG classification.
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Acknowledgment
This work was supported in part by the Ministry of Science and Technology in Taiwan under Grant MOST 107-2221-E-305-010-MY2.
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Chiu, PC., Cheng, HC., Yao, SN. (2020). Arrhythmia Detection Using Curve Fitting and Machine Learning. In: Lin, KP., Magjarevic, R., de Carvalho, P. (eds) Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices. ICBHI 2019. IFMBE Proceedings, vol 74. Springer, Cham. https://doi.org/10.1007/978-3-030-30636-6_41
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DOI: https://doi.org/10.1007/978-3-030-30636-6_41
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