发表机构
INESC TEC; Faculdade de Ciências, Universidade do Porto; LIACC, Universidade da Maia; Instituto Superior de Engenharia do Porto（INESC TEC; 波尔图大学理学院; 马亚大学LIACC; 波尔图工程高等学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出心音段选择算法与多输入CNN，同步分析多通道心音，在735例患者数据上实现96.5%准确率，优于单通道及异步多通道方法。
AI 中文摘要
心脏听诊仍是心血管疾病最具成本效益的筛查手段，需要在四个主要听诊点进行听诊。尽管如此，现有的自动心音分析算法大多使用单一心音（单通道）对患者进行分类，而当使用多个心音（多通道）时，也大多单独分析每个通道。据我们所知，尚无研究按照医生使用的流程，通过同步分析多通道心音对患者进行分类。这促使我们研究同步多通道分析是否优于单通道方法，以及是否比依次分析各通道的异步多通道方法更具优势，这种优势可能源于其能捕捉通道间的干扰现象。为回答这些问题，我们提出一种选择算法，用于从四个听诊点中识别最优心音段，随后将这些心音段输入多输入卷积神经网络（multi-input CNN），该网络通过同时分析四个选定的心音对患者进行分类。我们的同步方法结合所提出的选择算法与多输入卷积神经网络，实现了96.5%的总体准确率，比表现最佳的单通道和异步多通道方法高出9.1%。配对统计显著性检验（p=0.003）证实，所提出的心音段选择策略优于随机选择。上述结果基于CirCor DigiScope数据集中735名患者获得，这些患者具有四个听诊点的完整记录，我们结合该数据集及其他方法学考量对研究范围和可推广性进行了讨论。
英文摘要
Cardiac auscultation remains the most cost-effective screening procedure for cardiovascular diseases, and requires listening at the four main auscultation spots. Despite this, automatic heart sound analysis algorithms mostly classify patients using a single heart sound (single-channel), or, when using more than one (multi-channel), analyze each channel individually. To our knowledge, no prior work classifies patients through the synchronous analysis of multi-channel heart sounds, following the procedure used by physicians. This motivates us to study whether synchronous multi-channel analysis outperforms single-channel approaches, and whether it holds an advantage over asynchronous multi-channel methods that analyze channels one by one, potentially by capturing inter-channel interference phenomena. To answer these questions, we introduce a selection algorithm that identifies optimal heart sound segments from each of the four auscultation spots, which are then fed into a multi-input CNN that classifies patients by analyzing the four selected sounds simultaneously. Our synchronous approach, combining the proposed selection algorithm with a multi-input CNN, achieves a superior overall accuracy of 96.5\%, a 9.1\% gain over the best-performing single-channel and asynchronous multi-channel methods. The benefit of the proposed segment selection strategy over random selection is confirmed by a paired statistical significance test ($p = 0.003$). These results were obtained on 735 patients from the CirCor DigiScope dataset with complete recordings from all four spots, and their scope and generalizability are discussed in light of this and other methodological considerations.
