Abstract
The Electrocardiogram (ECG) can provide valuable information for medical diagnosis and diseases prevention. This paper presents an algorithm for arrhythmia classification based on frequency domain analysis of ECG biosignals. Our approach includes the use of Support Vector Machines (SVM) to identify seven different types of beats and four types of arrhythmia. Different feature sets were tested using the MIT-BIH arrhythmia database and a classification accuracy of 95.79 % was achieved.
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Acknowledgements
We thank Dr. Guillermo Mora of the Internal Medicine Department, Faculty of Medicine at National University of Colombia for his contributions in the understanding of the medical concepts behind an arrhythmia.
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Nivia, J.E., Ramírez, Y.M., Camargo, J.E. (2016). Arrhythmia Classification Using Biosignal Analysis and Machine Learning Techniques. In: Zheng, X., Zeng, D., Chen, H., Leischow, S. (eds) Smart Health. ICSH 2015. Lecture Notes in Computer Science(), vol 9545. Springer, Cham. https://doi.org/10.1007/978-3-319-29175-8_13
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DOI: https://doi.org/10.1007/978-3-319-29175-8_13
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