首个包含单独与混合心肺音的数字听诊数据集，助力智能诊断。
Manikin-Recorded Cardiopulmonary Sounds Dataset Using Digital Stethoscope
- 用数字听诊器采集模拟患者的心肺音，分部位记录。
- 涵盖正常及10种异常声音，含杂音、心律不齐等。
- 适合做AI听诊、音频分离和深度学习模型训练。
心肺音对医疗监测至关重要。近年来听诊技术进步使声音捕捉更精准。本数据集使用数字听诊器采集心肺音，包括单独与混合录制。据我们所知，这是首个同时提供分离与混合心血管呼吸音的数据集。录音来自临床模拟人（manikin），可模拟人体生理状态，在不同体表位置生成清晰心肺音。数据包含正常声音及多种异常（如杂音、房颤、心动过速、房室传导阻滞、第三/第四心音、哮鸣音、捻发音、鼾音、胸膜摩擦音、咕噜音）。每段录音由专科护士按解剖位置确定，经频域滤波增强特定音型。该数据集可用于人工智能应用，如自动心肺疾病检测、声音分类、无监督分离及音频信号处理深度学习算法。
原文摘要 · Abstract (English)
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.
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