The performance of cardiac arrhythmia detection with electrocardiograms(ECGs) has been considerably improved since the introduction of deep learning models. In practice, the high performance alone is not sufficient and a proper explanation is also required. Recently, researchers have started adopting feature attribution methods to address this requirement, but it has been unclear which of the methods are appropriate for ECG. In this work, we identify and customize three evaluation metrics for feature attribution methods based on the characteristics of ECG: localization score, pointing game, and degradation score. Using the three evaluation metrics, we evaluate and analyze eleven widely-used feature attribution methods. We find that some of the feature attribution methods are much more adequate for explaining ECG, where Grad-CAM outperforms the second-best method by a large margin.
翻译:自引入深层学习模式以来,心电图心脏心律失常检测的性能已大为改善,实际上,光靠高性能是不够的,还需要做出适当解释;最近,研究人员开始采用特征归属方法来满足这一要求,但不清楚哪些方法适合电心科。在这项工作中,我们根据特征归属方法的特征确定和定制三项评价指标:本地化评分、指针游戏和降解评分。我们利用三项评估指标,评估和分析11项广泛使用的特征归属方法。我们发现,有些特征归属方法更适合解释ECG, Grad-CAM在其中以大幅度比二流法更优的方法。