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arXiv Machine Learning By Felix Oury, Harley Day, Karina Mayoral, Ville-Pekka Sepp\"a, Sejal Saglani, Reiko J. Tanaka

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

Read the original on arXiv Machine Learning →

WIPSNet is a 3D ResNet that processes stacked continuous wavelet transform scalograms of overnight impedance pneumography (IP) signals to detect paediatric wheeze. In a study of 15 patients (60 nights, 281 hours), it achieved an AUC of 0.783 ± 0.026, outperforming the traditional Expiratory Variability Index, a state‑space model, and two modern sleep‑staging architectures. The model’s best performance occurs with a 32‑minute temporal context, highlighting the importance of multi‑scale temporal aggregation for nocturnal respiratory dynamics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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