用鼾声音频高召回率筛查睡眠呼吸暂停，适合大规模初筛。
A Recall-First CNN for Sleep Apnea Screening from Snoring Audio
- 将鼾声转为频谱图，通过过采样和类别权重平衡数据
- 对呼吸暂停事件检测召回率达90.55%，虽精度低但更重捕获率
- 可低成本用于家庭或基层医疗，早期发现高危人群
睡眠呼吸暂停是一种常见且严重的睡眠相关呼吸障碍，若未及时治疗将影响健康。目前临床诊断主要依赖夜间多导睡眠图（polysomnography），但该方法成本高、耗时长，难以用于大规模人群筛查。本文探索了一种更便捷的替代方案：利用呼吸音频识别呼吸暂停迹象。研究使用了18段音频文件，将呼吸声音转化为频谱图，通过过采样策略增强呼吸暂停片段，并引入类别权重以缓解多数类偏差。模型在呼吸暂停检测上达到90.55%的召回率，虽精确度较低，但强调捕捉异常事件的能力。结果表明，该方法具有作为低成本筛查工具的潜力，适用于居家或基础医疗场景，有助于早期识别高风险个体。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。