Luz Eduardo, Menotti David
Department of Computing, Universidade Federal de Ouro Preto, 35400-000 Ouro Preto, MG, Brazil.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4988-91. doi: 10.1109/IEMBS.2011.6091236.
Arrhythmia (i.e., irregular cardiac beat) classification in electrocardiogram (ECG) signals is an important issue for heart disease diagnosis due to the non-invasive nature of the ECG exam. In this paper, we analyze and criticize the results of some arrhythmia classification methods presented in the literature in terms of how the samples are chosen for training/testing the classifier and the impact this choice has on their performance (i.e., accuracy/sensitivity/specificity). From our implementation, we also report new accuracies for these methods, establishing a new state-of-the-art method, in terms of results.
由于心电图检查具有非侵入性，因此心电图（ECG）信号中的心律失常（即心跳不规则）分类是心脏病诊断的一个重要问题。在本文中，我们从为训练/测试分类器选择样本的方式以及这种选择对其性能（即准确性/敏感性/特异性）的影响方面，分析并批评了文献中提出的一些心律失常分类方法的结果。通过我们的实现，我们还报告了这些方法的新准确率，在结果方面建立了一种新的最先进方法。
