使用数字听诊器记录的体循环声音数据集
Manikin-Recorded Cardiopulmonary Sounds Dataset Using Digital Stethoscope
AI总结：
本文介绍了首个同时包含单独和混合心血管声音的数据集，用于AI在心脏病和肺部疾病自动检测中的应用。
摘要：
心肺声音对医疗监测至关重要。最近的听诊器技术进步使得能够更精确地捕捉患者声音成为可能。在本数据集中，我们使用数字听诊器捕捉了心脏和肺部声音，包括单独和混合录音。据我们所知，这是首个提供单独和混合心血管声音的数据集。录音是从临床假人中收集的，该假人设计用于复制人类生理条件，在不同身体位置生成干净的心肺声音。该数据集包括正常声音和各种异常（如杂音、心房颤动、心动过速、房室传导阻滞、第三和第四心音、哮鸣音、湿啰音、鼾声、胸膜摩擦音和咕噜声）。数据集包括在不同解剖位置进行的胸部检查的音频记录，由专科护士确定。每段录音都经过频率滤波增强以突出特定声音类型。该数据集可用于人工智能应用，如自动心脏病和肺部疾病检测、声音分类、无监督分离技术以及与音频信号处理相关的深度学习算法。
英文摘要：
Heart and lung sounds are crucial for healthcare monitoring. Recent improvements in stethoscope technology have made it possible to capture patient sounds with enhanced precision. In this dataset, we used a digital stethoscope to capture both heart and lung sounds, including individual and mixed recordings. To our knowledge, this is the first dataset to offer both separate and mixed cardiorespiratory sounds. The recordings were collected from a clinical manikin, a patient simulator designed to replicate human physiological conditions, generating clean heart and lung sounds at different body locations. This dataset includes both normal sounds and various abnormalities (i.e., murmur, atrial fibrillation, tachycardia, atrioventricular block, third and fourth heart sound, wheezing, crackles, rhonchi, pleural rub, and gurgling sounds). The dataset includes audio recordings of chest examinations performed at different anatomical locations, as determined by specialist nurses. Each recording has been enhanced using frequency filters to highlight specific sound types. This dataset is useful for applications in artificial intelligence, such as automated cardiopulmonary disease detection, sound classification, unsupervised separation techniques, and deep learning algorithms related to audio signal processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
