The gold-standard pneumotachograph is not routinely used to quantify airflow during overnight polysomnography due to the size, weight, bulkiness and discomfort of the equipment that must be worn. To overcome these deficiencies that have precluded the use of a pneumotachograph in routine sleep studies, our group developed a lightweight, low dead space 'pitot flowmeter' (based on pitot-tube principle) for use during sleep. We aimed to examine the characteristics and validate the flowmeter for quantifying airflow and detecting hypopneas during polysomnography by performing a head-to-head comparison with a pneumotachograph. Four experimental paradigms were utilized to determine the technical performance characteristics and the clinical usefulness of the pitot flowmeter in a head-to-head comparison with a pneumotachograph. In each study (1-4), the pitot flowmeter was connected in series with a pneumotachograph under either static flow (flow generator inline or on a face model) or dynamic flow (subject breathing via a polyester face model or on a nasal mask) conditions. The technical characteristics of the pitot flowmeter showed that, (1) the airflow resistance ranged from 0.065 ± 0.002 to 0.279 ± 0.004 cm H(2)O L(-1) s(-1) over the airflow rates of 10 to 50 L min(-1). (2) On the polyester face model there was a linear relationship between airflow as measured by the pitot flowmeter output voltage and the calibrated pneumotachograph signal a (β(1) = 1.08 V L(-1) s(-1); β(0) = 2.45 V). The clinically relevant performance characteristics (hypopnea detection) showed that (3) when the pitot flowmeter was connected via a mask to the human face model, both the sensitivity and specificity for detecting a 50% decrease in peak-to-peak airflow amplitude was 99.2%. When tested in sleeping human subjects, (4) the pitot flowmeter signal displayed 94.5% sensitivity and 91.5% specificity for the detection of 50% peak-to-peak reductions in pneumotachograph-measured airflow. Our data validate the pitot flowmeter for quantification of airflow and detecting breathing reduction during polysomnographic sleep studies. We speculate that quantifying airflow during sleep can differentiate phenotypic traits related to sleep disordered breathing.