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Detection of Atrial Fibrillation from Short ECGs: Minimalistic Complexity Analysis for Feature-Based Classifiers

机译:短心电图检测心房颤动:基于特征的分类器的最小复杂度分析

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In order to facilitate data-driven solutions for early detection of atrial fibrillation (AF), the 2017 CinC conference challenge was devoted to automatic AF classification based on short ECG recordings. The proposed solutions concentrated on maximizing the classifiers F1 score, whereas the complexity of the classifiers was not considered. However, we argue that this must be addressed as complexity places restrictions on the applicability of inexpensive devices for AF monitoring outside hospitals. Therefore, this study investigates the feasibility of complexity reduction by analyzing one of the solutions presented for the challenge.
机译:为了促进数据驱动的解决方案来及早发现心房颤动(AF),2017年CinC会议挑战赛致力于基于短ECG记录的自动AF分类。所提出的解决方案集中在最大化分类器F上 1 得分,而没有考虑分类器的复杂性。但是,我们认为必须解决这一问题,因为复杂性限制了廉价设备在医院外进行房颤监测的适用性。因此,本研究通过分析针对挑战提出的解决方案之一,研究了降低复杂性的可行性。

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