Non-invasive continuous respiratory monitoring using temperature-based sensors

• Published in: Journal of clinical monitoring and computing Vol. 34; no. 2; pp. 223 - 231
• Main Authors: Hurtado, Daniel E., Abusleme, Angel, Chávez, Javier A. P.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.04.2020; Springer Nature B.V
• Subjects: Adolescent; Adult; Aged; Air flow; Anesthesiology; Breathing; Confidence intervals; Critical Care Medicine; Equipment Design; Female; Health Sciences; Humans; Intensive; Male; Medicine; Medicine & Public Health; Middle Aged; Monitoring; Monitoring, Physiologic - instrumentation; Monitoring, Physiologic - methods; Monitoring, Physiologic - statistics & numerical data; Original Research; Position sensing; Respiration; Respiratory Rate; Sensors; Signal-To-Noise Ratio; Statistics for Life Sciences; Temperature; Temperature sensors; Young Adult; Thermal sensors; Respiration; Monitoring
• ISSN: 1387-1307; 1573-2614; 1573-2614
• DOI: 10.1007/s10877-019-00329-5

Respiratory rate (RR) is a key vital sign that has been traditionally employed in the clinical assessment of patients and in the prevention of respiratory compromise. Despite its relevance, current practice for monitoring RR in non-intubated patients strongly relies on visual counting, which delivers an intermittent and error-prone assessment of the respiratory status. Here, we present a novel non-invasive respiratory monitor that continuously measures the RR in human subjects. The respiratory activity of the user is inferred by sensing the thermal transfer between the breathing airflow and a temperature sensor placed between the nose and the mouth. The performance of the respiratory monitor is assessed through respiratory experiments performed on healthy subjects. Under spontaneous breathing, the mean RR difference between our respiratory monitor and visual counting was 0.4 breaths per minute (BPM), with a 95% confidence interval equal to [− 0.5, 1.3] BPM. The robustness of the respiratory sensor to the position is assessed by studying the signal-to-noise ratio in different locations on the upper lip, displaying a markedly better performance than traditional thermal sensors used for respiratory airflow measurements.