Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:1976-1979. doi: 10.1109/EMBC46164.2021.9629714.
Human auscultation has been regarded as a cheap, convenient and efficient method for the diagnosis of cardiovascular diseases. Nevertheless, training professional auscultation skills needs tremendous efforts and is time-consuming. Computer audition (CA) that leverages the power of advanced machine learning and signal processing technologies has increasingly attracted contributions to the field of automatic heart sound classification. While previous studies have shown promising results in CA based heart sound classification with the 'shuffle split' method, machine learning for heart sound classification decreases in accuracy with a cross-corpus test dataset. We investigate this problem with a cross-corpus evaluation using the PhysioNet CinC Challenge 2016 Dataset and propose a new combination of data augmentation techniques that leads to a CNN robust for such cross-corpus evaluation. Compared with the baseline, which is given without augmentation, our data augmentation techniques combined improve by 20.0 % the sensitivity and by 7.9 % the specificity on average across 6 databases, which is a significant difference on 4 out of these (p < .05 by one-tailed z-test).
人工听诊一直被认为是诊断心血管疾病的一种廉价、方便和有效的方法。然而，训练专业的听诊技能需要巨大的努力和时间。利用先进的机器学习和信号处理技术的计算机听诊 (CA) 越来越受到自动心音分类领域的关注。虽然以前的研究已经表明，在基于“洗牌分割”方法的心音分类中，CA 具有很有前景的结果，但是跨语料库测试数据集的心音分类机器学习的准确性会降低。我们使用 PhysioNet CinC 挑战赛 2016 数据集进行了跨语料库评估来研究这个问题，并提出了一种新的数据增强技术组合，使 CNN 能够对这种跨语料库评估具有鲁棒性。与没有增强的数据的基线相比，我们的数据增强技术组合平均提高了 6 个数据库中 20.0%的敏感性和 7.9%的特异性，其中 4 个数据库的差异有统计学意义（单侧 z 检验，p<0.05）。
