标题： 一种基于召回的CNN用于从打鼾音频中进行睡眠呼吸暂停筛查 显示英文标题

标题： A Recall-First CNN for Sleep Apnea Screening from Snoring Audio

摘要： 睡眠呼吸暂停是一种严重的与睡眠相关的呼吸障碍，很常见，如果不治疗会影响健康。 目前传统的筛查和诊断方法是整夜多导睡眠图。 多导睡眠图价格昂贵且耗时较长，在大规模人群筛查中不切实际。 在本文中，我们探索了一种更易获取的选项，使用呼吸音频记录来检测呼吸暂停的迹象。我们利用了18个音频文件。该方法包括将呼吸声音转换为频谱图，通过过采样呼吸暂停段来平衡数据集，并应用类别权重以减少对多数类的偏差。 该模型在呼吸暂停检测中的召回率为90.55。 有意地，优先捕捉呼吸暂停事件而非整体准确性。 尽管精确度较低，但高召回率表明其作为低成本筛查工具的潜力，可以在家中或基本临床环境中使用，可能有助于更早地识别高风险个体。 显示英文摘要

摘要： Sleep apnea is a serious sleep-related breathing disorder that is common and can impact health if left untreated. Currently the traditional method for screening and diagnosis is overnight polysomnography. Polysomnography is expensive and takes a lot of time, and is not practical for screening large groups of people. In this paper, we explored a more accessible option, using respiratory audio recordings to spot signs of apnea.We utilized 18 audio files.The approach involved converting breathing sounds into spectrograms, balancing the dataset by oversampling apnea segments, and applying class weights to reduce bias toward the majority class. The model reached a recall of 90.55 for apnea detection. Intentionally, prioritizing catching apnea events over general accuracy. Despite low precision,the high recall suggests potential as a low-cost screening tool that could be used at home or in basic clinical setups, potentially helping identify at-risk individuals much earlier.