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<p>The Flow Regulated Nasal Delivery System (FRNDS) is a novel smart nasal cannula platform developed to enable noninvasive, continuous monitoring of tidal volume (VT) and detection of abnormal breathing events. Combined with peripheral capillary oxygen saturation (SpO₂) data modulation of oxygen flow can be accomplished. In a cohort of 57 adults, FRNDS-derived VT and minute ventilation were compared to reference measurements from respiratory inductance plethysmography (RIP) across a range of oxygen flow rates. While the cannula system tended to overestimate inspiratory and underestimate expiratory VT—especially at higher flow rates—these errors were substantially reduced by applying machine learning regression models trained on anatomical and physiological features, achieving strong agreement with RIP averaging a 53.5 mL error, 78.2 mL root-mean error, and is within ~ 11% of the actual value. The system also demonstrated robust performance in classifying clinically relevant breathing patterns, including apneic spells and mouth breathing, suggesting utility for real-time respiratory surveillance and sleep apnea detection. These results support FRNDS as a promising, adaptable solution for individualized respiratory monitoring in both clinical and home environments.</p>

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Continuous non-invasive measurement of tidal volume and minute ventilation using a smart nasal cannula

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月27日

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  • Alan B. Dogan,
  • Neel Patel,
  • Carter Gottschalk,
  • Rae L. Blankenship,
  • Valerie K. Young,
  • Alfred Wicks,
  • Umar F. Sofi

摘要

The Flow Regulated Nasal Delivery System (FRNDS) is a novel smart nasal cannula platform developed to enable noninvasive, continuous monitoring of tidal volume (VT) and detection of abnormal breathing events. Combined with peripheral capillary oxygen saturation (SpO₂) data modulation of oxygen flow can be accomplished. In a cohort of 57 adults, FRNDS-derived VT and minute ventilation were compared to reference measurements from respiratory inductance plethysmography (RIP) across a range of oxygen flow rates. While the cannula system tended to overestimate inspiratory and underestimate expiratory VT—especially at higher flow rates—these errors were substantially reduced by applying machine learning regression models trained on anatomical and physiological features, achieving strong agreement with RIP averaging a 53.5 mL error, 78.2 mL root-mean error, and is within ~ 11% of the actual value. The system also demonstrated robust performance in classifying clinically relevant breathing patterns, including apneic spells and mouth breathing, suggesting utility for real-time respiratory surveillance and sleep apnea detection. These results support FRNDS as a promising, adaptable solution for individualized respiratory monitoring in both clinical and home environments.