Castro Ana, Vinhoza Tiago T V, Mattos Sandra S, Coimbra Miguel T
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:3909-12. doi: 10.1109/EMBC.2013.6610399.
Auscultation is widely applied in clinical activity, nonetheless sound interpretation is dependent on clinician training and experience. Heart sound features such as spatial loudness, relative amplitude, murmurs, and localization of each component may be indicative of pathology. In this study we propose a segmentation algorithm to extract heart sound components (S1 and S2) based on it's time and frequency characteristics. This algorithm takes advantage of the knowledge of the heart cycle times (systolic and diastolic periods) and of the spectral characteristics of each component, through wavelet analysis. Data collected in a clinical environment, and annotated by a clinician was used to assess algorithm's performance. Heart sound components were correctly identified in 99.5% of the annotated events. S1 and S2 detection rates were 90.9% and 93.3% respectively. The median difference between annotated and detected events was of 33.9 ms.
听诊在临床活动中被广泛应用，然而声音的解读依赖于临床医生的培训和经验。心音特征，如空间响度、相对振幅、杂音以及每个成分的定位，可能提示病理情况。在本研究中，我们提出一种分割算法，基于心音的时间和频率特征来提取心音成分（S1和S2）。该算法通过小波分析，利用心动周期时间（收缩期和舒张期）以及每个成分的频谱特征方面的知识。在临床环境中收集并经临床医生标注的数据用于评估算法的性能。在99.5%的标注事件中，心音成分被正确识别。S1和S2的检测率分别为90.9%和93.3%。标注事件与检测事件之间的中位数差异为33.9毫秒。
