Blood Pressure Estimation Using Explainable Deep-Learning Models Based on Photoplethysmography.

Journal: Anesthesia and analgesia
PMID: 39680992

Abstract

BACKGROUND: Due to their invasiveness, arterial lines are not typically used in routine monitoring, despite their superior responsiveness in hemodynamic monitoring and detecting intraoperative hypotension. To address this issue, noninvasive, continuous arterial pressure monitoring is necessary. We developed a deep-learning model that reconstructs continuous mean arterial pressure (MAP) using the photoplethysmograhy (PPG) signal and compared it to the arterial line gold standard.

Authors

  • Jade Perdereau
    From the Université Paris Cité, INSERM UMRS 942 (MASCOT), Paris, France.
  • Thibaut Chamoux
    Entrepôt de données de santé, Assistance Publique Hôpitaux de Paris, Paris, France.
  • Etienne Gayat
    Department of Anesthesia Burn and Critical Care, University Hospitals Saint-Louis-Lariboisière, AP-HP, Paris, France.
  • Arthur Le Gall
    Department of Anesthesia and Critical Care, Lariboisière Hospital, APHP, Paris, France.
  • Fabrice Vallée
    From the Université Paris Cité, INSERM UMRS 942 (MASCOT), Paris, France.
  • Jérôme Cartailler
    From the Université Paris Cité, INSERM UMRS 942 (MASCOT), Paris, France.
  • Jona Joachim
    From the Université Paris Cité, INSERM UMRS 942 (MASCOT), Paris, France.

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