WIPSNet: Deep Learning for Paediatric Wheeze Detection from Overnight Impedance Pneumography

arXiv:2610.00398v1 Announce Type: new Abstract: 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.

Related

• AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts
• Honest and Reliable Evaluation and Expert Equivalence Testing of Automated Neonatal Seizure Detection
• Deep learning-based detection of cessation of breathing in pre-term infants
• From Stress to Affect: Multimodal Deep Learning for Physiological Emotion Recognition Across Wearable Sensor Modalities

Source: arXiv cs.LG | 2026-10-02