In this paper, we studied the improvement in heartbeat classification achieved by including information from multilead ECG recordings in a previously developed and validated classification model. This model includes features from the RR interval series and morphology descriptors for each lead calculated from the wavelet transform. The experiments were carried out in the INCART database, available in Physionet, and the generalization was corroborated in private and public databases. In all databases, the AAMI recommendations for class labeling and results presentation were followed. Different strategies to integrate the additional information available in the 12-leads were studied. The best performing strategy consisted in performing principal component analysis to the wavelet transform of the available ECG leads. The performance indices obtained for normal beats were sensitivity ( $S$) 98%, positive predictive value ( $P^{+}$) 93%; for supraventricular beats, ( $S$) 86%, ($P^{+}$ ) 91%; and for ventricular beats ($S$) 90%, ($P^{+}$) 90%. The generalization capability of the chosen strategy was confirmed by applying the classifier to other databases with different number of leads with comparable results. In conclusion, the performance of the reference two-lead classifier was improved by taking into account additional information from the 12-leads.
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机译:在本文中,我们研究了通过将多导联心电图记录中的信息包含在先前开发和验证的分类模型中而实现的心跳分类的改进。该模型包括来自RR间隔序列的特征和从小波变换计算出的每个导联的形态描述符。实验在Physionet的INCART数据库中进行,泛化在私有和公共数据库中得到证实。在所有数据库中,都遵循了AAMI关于类别标签和结果表示的建议。研究了整合12条线索中可用附加信息的不同策略。最佳执行策略包括对可用ECG导联的小波变换执行主成分分析。正常心跳获得的性能指标为灵敏度($ S $)98%,阳性预测值($ P ^ {+} $)93%;对于室上搏动,($ S $)86%,($ P ^ {+} $)91%;对于心室搏动($ S $)为90%,($ P ^ {+} $)为90%。通过将分类器应用于具有不同潜在客户数量且结果可比的其他数据库,可以确认所选策略的泛化能力。总之,通过考虑来自12导联的其他信息,改进了参考两导联分类器的性能。
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