Abstract
Atrial Fibrillation AF reported as the most occurring heart arrhythmia. Steadfast detection of AF in ECG monitoring systems is considerable for early treatment and health risks reduction. Various ECG mining and analysis efforts have addressed a wide variety of technical issues. However, the morphological descriptors are changing along the time within the different patients. As a result, the classification model constructed using old training data is not accurate enough to detect AF. This paper presents an outstanding dynamic learning method to achieve better AF arrhythmia detection in real-time applications. The performance of our proposed technique showed 96.2%, 99.7%, and 99.4% for sensitivity, specificity, and overall accuracy, respectively. Accordingly, the proposed Cache learning method can be introduced to improve the performance of the AF intelligent detection systems.
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Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2019K2A9A2A06020672 and No. 2020R1A2B5B02001717).
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Bashir, M.E.A., Mohamed, A.H.H.M., Khanan, A., Fattah, F.A.M.A., Wang, L., Ryu, K.H. (2021). Cache Learning Method for Terrific Detection of Atrial Fibrillation. In: Pan, JS., Li, J., Namsrai, OE., Meng, Z., Savić, M. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 211. Springer, Singapore. https://doi.org/10.1007/978-981-33-6420-2_62
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DOI: https://doi.org/10.1007/978-981-33-6420-2_62
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