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.
翻译:事实证明,在临床上,心声疗法有助于临床使用心血管疾病的早期筛查。由于对受过良好训练的专业人员的早期治疗需求很高,受益于信号处理和机器学习的自动治疗可以帮助辅助诊断,减轻专业临床医生的培训负担。然而,经典机器学习仅限于大数据时代的性能改进。在许多研究领域,深层学习比经典机器学习取得更好的业绩,因为它使用较复杂的模型结构,具有较强的提取有效表现能力。深层学习在过去几年中成功地应用于心脏健康分析。由于大多数关于心脏健康分析的审查工作是在2017年之前进行的,本次调查是首次全面概述工作,以总结关于心脏健康分析的文件,并在2017-2022年的六年中进行深入学习。我们介绍经典机器学习和深层学习,以便进行比较,并进一步介绍深层学习的进展和未来研究方向,用于心脏健康分析。