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Deep learning models improve breathing cessation detection in preterm infants

By PulseAugur Editorial · [2 sources] ·

Researchers have developed deep learning models to more accurately detect cessation of breathing events in preterm infants within neonatal intensive care units. These models, utilizing impedance pneumography (IP), electrocardiography (ECG), and photoplethysmography (PPG) signals, demonstrated improved performance over traditional threshold-based monitoring. A ConvNeXt architecture combining IP and PPG signals achieved the highest balanced accuracy of 88.7%, highlighting the effectiveness of deep learning in analyzing complex physiological data for critical infant monitoring. AI

IMPACT Enhances accuracy in critical infant monitoring, potentially reducing false alarms and improving patient outcomes.

RANK_REASON The cluster contains two academic papers detailing research on a deep learning model for medical applications.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Deep learning models improve breathing cessation detection in preterm infants

COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Deep learning-based detection of cessation of breathing in pre-term infants

    Apnoea of prematurity is characterised by recurrent episodes of cessation of breathing and remains difficult to detect reliably using routinely monitored physiological signals in the Neonatal Intensive Care Unit (NICU). Existing bedside monitors rely primarily on respiratory rate…

  2. arXiv cs.LG TIER_1 English(EN) · Mauricio Villarroel ·

    Deep learning-based detection of cessation of breathing in pre-term infants

    Apnoea of prematurity is characterised by recurrent episodes of cessation of breathing and remains difficult to detect reliably using routinely monitored physiological signals in the Neonatal Intensive Care Unit (NICU). Existing bedside monitors rely primarily on respiratory rate…

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