WIPSNet：基于夜间阻抗呼吸描记术的儿科喘息检测深度学习
WIPSNet: Deep Learning for Paediatric Wheeze Detection from Overnight Impedance Pneumography
- Imperial College London（伦敦帝国理工学院）
- Royal Brompton Hospital（皇家布朗普顿医院）
- Icare Finland Ltd（Icare芬兰有限公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对夜间阻抗呼吸描记术的儿科喘息检测，提出WIPSNet（3D ResNet）处理连续小波尺度图，在15名患者队列上AUC达0.783，优于EVI等基线，证明时频表示与3D卷积的有效性。
AI中文摘要：
夜间阻抗呼吸描记术（IP）用于监测儿科呼吸健康。其当前的临床读数——呼气变异性指数（EVI）——将每次IP记录压缩为单个标量，并在夜间级喘息分类中达到0.633的AUC。我们引入了喘息阻抗呼吸描记术尺度图网络（WIPSNet），这是一个3D ResNet，作用于夜间IP信号的堆叠连续小波变换尺度图。在15名患者队列（60个夜晚，281小时）上，WIPSNet达到0.783±0.026的AUC，优于EVI、状态空间模型（Mamba）以及两种现代睡眠分期架构。性能在对应32分钟时间上下文的体积深度处达到峰值，表明多尺度时间聚合对于建模夜间呼吸动力学很重要。总体而言，这些结果表明，结构化时频表示结合3D卷积架构为从长且不规则的生理时间序列中学习提供了有效方法。
英文摘要：
Overnight impedance pneumography (IP) is used to monitor paediatric respiratory health. Its current clinical readout, the Expiratory Variability Index (EVI), compresses each IP recording into a single scalar and achieves an AUC of 0.633 for night-level wheeze classification. We introduce Wheeze Impedance Pneumography Scalogram Network (WIPSNet), a 3D ResNet operating on stacked continuous wavelet transform scalograms of overnight IP signals. On a 15-patient cohort (60 nights, 281 hours), WIPSNet achieves an AUC of $0.783 \pm 0.026$, outperforming EVI, a state-space model (Mamba), and two modern sleep-staging architectures. Performance peaks at a volumetric depth corresponding to 32 minutes of temporal context, suggesting that multi-scale temporal aggregation is important for modelling nocturnal respiratory dynamics. Overall, these results indicate that structured time-frequency representations combined with 3D convolutional architectures provide an effective approach for learning from long, irregular physiological time series.
