基于心音信号的自监督模型，可自动筛查心脏功能异常。
CardioPHON: Quality assessment and self-supervised pretraining for screening of cardiac function based on phonocardiogram recordings
- 自监督预训练结合多模态特征，提升心音分类性能。
- 在2022年PhysioNet挑战赛中，多模态模型排名第一，单模态模型位列第四。
- 首个公开的心音预训练模型，助力小样本心血管诊断应用。
远程监测心血管疾病对早期发现心脏功能异常至关重要，有助于及时干预、改善预防性护理并实现个性化治疗。通过计算机辅助决策系统可自动检测心音异常，作为心血管问题的初筛工具或治疗效果监测手段。本文提出CardioPHON，一个集成心音质量评估与分类的工具，用于从心音图记录中筛查异常心脏功能。该模型在六个中小型心音数据集上进行自监督预训练，可自动剔除低质量录音，确保细微异常不被误诊，并在心音分类任务中达到顶尖性能。融合音频与社会人口学特征的多模态模型在2022年George B. Moody PhysioNet心音挑战赛官方排行榜中位居第一；仅使用心音图的单模态模型则在同类方法中排名第一（总排名第四），优于采用多模态的模型。CardioPHON是心音领域首个公开发布的预训练模型，推动了数据高效人工智能模型的发展，可泛化至多种心血管诊断下游任务。
原文摘要 · Abstract (English)
Remote monitoring of cardiovascular diseases plays an essential role in early detection of abnormal cardiac function, enabling timely intervention, improved preventive care, and personalized patient treatment. Abnormalities in the heart sounds can be detected automatically via computer-assisted decision support systems, and used as the first-line screening tool for detection of cardiovascular problems, or for monitoring the effects of treatments and interventions. We propose in this paper CardioPHON, an integrated heart sound quality assessment and classification tool that can be used for screening of abnormal cardiac function from phonocardiogram recordings. The model is pretrained in a self-supervised fashion on a collection of six small- and mid-sized heart sound datasets, enables automatic removal of low quality recordings to ensure that subtle sounds of heart abnormalities are not misdiagnosed, and provides a state-of-the-art performance for the heart sound classification task. The multimodal model that combines audio and socio-demographic features demonstrated superior performance, achieving the best ranking on the official leaderboard of the 2022 George B. Moody PhysioNet heart sound challenge, whereas the unimodal model, that is based only on phonocardiogram recordings, holds the first position among the unimodal approaches (a total rank 4), surpassing the models utilizing multiple modalities. CardioPHON is the first publicly released pretrained model in the domain of heart sound recordings, facilitating the development of data-efficient artificial intelligence models that can generalize to various downstream tasks in cardiovascular diagnostics.
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