Abstract
Heart sound auscultation has been demonstrated to be beneficial in clinical usage for early screening of cardiovascular diseases. Due to the high requirement of well-trained professionals for auscultation, automatic auscultation benefiting from signal processing and machine learning can help auxiliary diagnosis and reduce the burdens of training professional clinicians. Nevertheless, classic machine learning is limited to performance improvement in the era of big data. Deep learning has achieved better performance than classic machine learning in many research fields, as it employs more complex model architectures with stronger capability of extracting effective representations. Deep learning has been successfully applied to heart sound analysis in the past years. As most review works about heart sound analysis were given before 2017, the present survey is the first to work on a comprehensive overview to summarise papers on heart sound analysis with deep learning in the past six years 2017--2022. We introduce both classic machine learning and deep learning for comparison, and further offer insights about the advances and future research directions in deep learning for heart sound analysis.
Abstract (translated)
心脏听诊在临床实践中用于早期心血管疾病筛查方面显示出有益效果。由于对听诊专业素质的高要求,信号处理和机器学习可以帮助改善听诊效果,有助于辅助诊断并减轻训练专业临床医生的工作压力。然而,经典机器学习只能在大数据时代提高性能。深度学习在许多研究领域取得了比经典机器学习更好的表现,因为它使用更复杂的模型架构,具有更强的提取有效表示的能力。深度学习已经在过去几年中成功地应用于心脏听诊分析。由于大多数关于心脏听诊的综述工作都是在2017年以前完成的,本调查是首先尝试进行全面综述,总结2017-2022年期间使用深度学习的心脏听诊分析论文的。我们介绍了经典机器学习和深度学习进行比较,并进一步提供了深度学习在心脏听诊分析方面的最新进展和未来研究方向的见解。
URL
https://arxiv.org/abs/2301.09362
