Pneumatic sensor for cardiorespiratory monitoring during sleep

Abstract

We show that a non-intrusive pneumatic sensor can be used to measure respiratory rate, heart rate, and their variability during sleep. The pneumatic sensor was included in a polysomnography (PSG) study involving 42 participants in a sleep laboratory. The agreement between the pneumatic sensor and the PSG for respiratory rate, heart rate, and their variability was quantified by Bland-Altman analysis. The respiratory rate has a mean value of 15.4 breaths per minute for a bias of −0.06 and 95% limits of agreement (LOA) of [−0.6; 0.5] breath per minute. The respiratory rate variability root mean square of successive differences (RMSSD) has a mean value of 459.51 ms, a bias of 9.2 and a 95% LOA of [−43.5; 61.9] ms. The heart rate has a mean value of 60.6 beats per minute for a bias of −0.8 and a 95% LOA of [−4.3; 2.7] beat per minute. The heart rate variability RMSSD has a mean value of 44.1 ms, a bias of 14.7 and a 95% LOA of [−19; 48.4] ms. These results show that a non-intrusive pneumatic sensor can accurately estimate cardiorespiratory metrics overnight.

Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea

“…These signals are used to estimate sleep macrostructure (total sleep time), sleep timing (bedtime and wake-time), and the AHI using automated algorithms ( see the online supplement for a more detailed technology description). Clinical validation shows good agreement with polysomnography-derived AHI ( 16 , 17 ) with high predictive performance (88% sensitivity and 88% specificity) to classify moderate to severe OSA (⩾15 events/h sleep). A further internal validation study in 32 participants (26 men and 6 women) independently studied at the Adelaide Institute for Sleep Health laboratory ( see the online supplement) showed similar diagnostic performance characteristics.…”

Noncontact Longitudinal Respiratory Rate Measurements in Healthy Adults Using Radar-Based Sleep Monitor (Somnofy): Validation Study

“…Comparison between studies is always difficult, because the sleep data, reference device or signal processing, and performance metrics are different. However, to the best of our knowledge, the results of this study are significantly better than those of other technologies [23][24][25][44][45][46] including radar technology [27,29]. The validated measurements are also more instantaneous, as previous studies have averaged RRs over epochs of data.…”

“…The validated measurements are also more instantaneous, as previous studies have averaged RRs over epochs of data. Moreover, previous studies of noncontact RR measurements during sleep have not investigated the effect of all the factors that can affect the results, such as sleep stage, age, BMI, and sleeping position [23][24][25]27,46]. This is also the first study that explicitly analyses coverage and measures gaps in continuous RR measurements during sleep using radar technology.…”

“…Nocturnal RRs have also been shown to be an independent predictor of long-term mortality (RRs >16 respirations [breaths] per minute [RPM]) [21], and nocturnal hyperpnea is shown to be an indicator of periodic limb movement disorders [22]. Different under-the-mattress sensors have been investigated for this purpose [23][24][25], but radar technology is also an alternative.…”

A review of wearable and unobtrusive sensing technologies for chronic disease management

“…7 (a)). The BCG signal could also be acquired by pneumatic sensors [ 98 ], optical fibers [ 99 ], hydraulic bed sensors [ 100 ], [ 101 ] ( Fig. 7 (b)) and accelerometers [ 102 ].…”