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

WIPSNet, a 3D ResNet operating on stacked continuous wavelet transform scalograms of overnight impedance pneumography (IP) signals, achieved an AUC of 0.783 ± 0.026 for night-level paediatric wheeze classification, according to a new arXiv paper (arXiv:2610.00398v1). The model outperformed the current clinical readout, the Expiratory Variability Index (EVI), which scores an AUC of 0.633, as well as a state-space model (Mamba) and two modern sleep-staging architectures on a 15-patient cohort of 60 nights and 281 hours. Performance peaked at a volumetric depth corresponding to 32 minutes of temporal context, indicating that multi-scale temporal aggregation matters for modelling nocturnal respiratory dynamics.

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.