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A cascaded classifier for multi-lead ECG based on feature fusion

机译:基于特征融合的多引导ECG级联分类器

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摘要

Background and Objective: Electrocardiogram (ECG) is an important diagnostic tool for the diagnosis of heart disorders. Useful features and well-designed classification method are crucial for automatic diagnosis. However, most of the contributions were in single lead or two-lead ECG signal and only features from single lead were used to classify the ECG beats. In this paper, a cascaded classification system is proposed to extract features and classify heartbeats in order to improve the performance of ECG beat classification via multi-lead ECG.
机译:背景和目的:心电图(ECG)是诊断心脏病的重要诊断工具。 有用的功能和设计精心设计的分类方法对于自动诊断至关重要。 然而,大多数贡献都是单一的或两种引导ECG信号,并且仅使用单引线的功能来分类ECG节拍。 在本文中,提出了一种级联分类系统来提取特征和分类心跳,以通过多引导ECG提高ECG击败分类的性能。

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