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    Cardiac Arrhythmia Classification Based on One-Dimensional Morphological Features by Heechang Lee, Taeyoung Yoon, Chaeyun Yeo, HyeonYoung Oh, Yebin Ji, Seongwoo Sim, Daesung Kang

    Published 2021-10-01
    “…Experiments were conducted on a 12-lead ECG database collected from Chapman University and Shaoxing People’s Hospital. Of the six machine learning algorithms, combining XGBoost with the proposed features yielded an accuracy of 90.46%, an AUC of 0.982, a sensitivity of 0.892, a precision of 0.900, and an F1 score of 0.895 and presented better results than wavelet features with XGBoost. …”
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