Patricia Boyle

Sleep/wake measurement using a non-contact biomotion sensor

We studied a novel non‐contact biomotion sensor, which has been developed for identifying sleep/wake patterns in adult humans. The biomotion sensor uses ultra low‐power reflected radiofrequency waves to determine the movement of a subject during sleep. An automated classification algorithm has been developed to recognize sleep/wake states on a 30‐s epoch basis based on the measured movement signal. The sensor and software were evaluated against gold‐standard polysomnography on a database of 113 subjects [94 male, 19 female, age 53 ± 13 years, apnoea–hypopnea index (AHI) 22 ± 24] being assessed for sleep‐disordered breathing at a hospital‐based sleep laboratory. The overall per‐subject accuracy was 78%, with a Cohen’s kappa of 0.38. Lower accuracy was seen in a high AHI group (AHI >15, 63 subjects) than in a low AHI group (74.8% versus 81.3%); however, most of the change in accuracy can be explained by the lower sleep efficiency of the high AHI group. Averaged across subjects, the overall sleep sensitivity was 87.3% and the wake sensitivity was 50.1%. The automated algorithm slightly overestimated sleep efficiency (bias of +4.8%) and total sleep time (TST; bias of +19 min on an average TST of 288 min). We conclude that the non‐contact biomotion sensor can provide a valid means of measuring sleep–wake patterns in this patient population, and also allows direct visualization of respiratory movement signals.

SleepMinder: An innovative contact-free device for the estimation of the apnoea-hypopnoea index

We describe an innovative sensor technology (SleepMinder™) for contact-less and convenient measurement of sleep and breathing in the home. The system is based on a novel non-contact biomotion sensor and proprietary automated analysis software. The biomotion sensor uses an ultra low-power radio-frequency transceiver to sense the movement and respiration of a subject. Proprietary software performs a variety of signal analysis tasks including respiration analysis, sleep quality measurement and sleep apnea assessment. This paper measures the performance of SleepMinder as a device for the monitoring of sleep-disordered breathing (SDB) and the provision of an estimate of the apnoea-hypopnoea index (AHI). The SleepMinder was tested against expert manually scored PSG data of patients gathered in an accredited sleep laboratory. The comparison of SleepMinder to this gold standard was performed across overnight recordings of 129 subjects with suspected SDB. The dataset had a wide demographic profile with the age ranging between 20 and 81 years. Body weight included subjects with normal weight through to the very obese (Body Mass Index: 21-44 kg/m2). SDB severity ranged from subjects free of SDB to those with severe SDB (AHI: 0.8-96 events/hours). SleepMinders AHI estimation has a correlation of 91% and can detect clinically significant SDB (AHI>15) with a sensitivity of 89% and a specificity of 92%.

Assessment of sleep-disordered breathing using a non-contact bio-motion sensor

Obstructive sleep apnoea is a highly prevalent but under‐diagnosed disorder. The gold standard for diagnosis of obstructive sleep apnoea is inpatient polysomnography. This is resource intensive and inconvenient for the patient, and the development of ambulatory diagnostic modalities has been identified as a key research priority. SleepMinder (BiancaMed, NovaUCD, Ireland) is a novel, non‐contact, bedside sensor, which uses radio‐waves to measure respiration and movement. Previous studies have shown it to be effective in measuring sleep and respiration. We sought to assess its utility in the diagnosis of obstructive sleep apnoea. SleepMinder and polysomnographic assessment of sleep‐disordered breathing were performed simultaneously on consecutive subjects recruited prospectively from our sleep clinic. We assessed the diagnostic accuracy of SleepMinder in identifying obstructive sleep apnoea, and how SleepMinder assessment of the apnoea–hypopnoea index correlated with polysomnography. Seventy‐four subjects were recruited. The apnoea–hypopnoea index as measured by SleepMinder correlated strongly with polysomnographic measurement (r = 0.90; P ≤ 0.0001). When a diagnostic threshold of moderate–severe (apnoea–hypopnoea index ≥15 events h−1) obstructive sleep apnoea was used, SleepMinder displayed a sensitivity of 90%, a specificity of 92% and an accuracy of 91% in the diagnosis of sleep‐disordered breathing. The area under the curve for the receiver operator characteristic was 0.97. SleepMinder correctly classified obstructive sleep apnoea severity in the majority of cases, with only one case different from equivalent polysomnography by more than one diagnostic class. We conclude that in an unselected clinical population undergoing investigation for suspected obstructive sleep apnoea, SleepMinder measurement of sleep‐disordered breathing correlates significantly with polysomnography.

Comparison of a novel non-contact biomotion sensor with wrist actigraphy in estimating sleep quality in patients with obstructive sleep apnoea.

Ambulatory monitoring is of major clinical interest in the diagnosis of obstructive sleep apnoea syndrome. We compared a novel non‐contact biomotion sensor, which provides an estimate of both sleep time and sleep‐disordered breathing, with wrist actigraphy in the assessment of total sleep time in adult humans suspected of obstructive sleep apnoea syndrome. Both systems were simultaneously evaluated against polysomnography in 103 patients undergoing assessment for obstructive sleep apnoea syndrome in a hospital‐based sleep laboratory (84 male, aged 55 ± 14 years and apnoea–hypopnoea index 21 ± 23). The biomotion sensor demonstrated similar accuracy to wrist actigraphy for sleep/wake determination (77.3%: biomotion; 76.5%: actigraphy), and the biomotion sensor demonstrated higher specificity (52%: biomotion; 34%: actigraphy) and lower sensitivity (86%: biomotion; 94%: actigraphy). Notably, total sleep time estimation by the biomotion sensor was superior to actigraphy (average overestimate of 10 versus 57 min), especially at a higher apnoea–hypopnoea index. In post hoc analyses, we assessed the improved apnoea–hypopnoea index accuracy gained by combining respiratory measurements from polysomnography for total recording time (equivalent to respiratory polygraphy) with total sleep time derived from actigraphy or the biomotion sensor. Here, the number of misclassifications of obstructive sleep apnoea severity compared with full polysomnography was reduced from 10/103 (for total respiratory recording time alone) to 7/103 and 4/103 (for actigraphy and biomotion sensor total sleep time estimate, respectively). We conclude that the biomotion sensor provides a viable alternative to actigraphy for sleep estimation in the assessment of obstructive sleep apnoea syndrome. As a non‐contact device, it is suited to longitudinal assessment of sleep, which could also be combined with polygraphy in ambulatory studies.

Real time breathing rate estimation from a non contact biosensor

An automated real time method for detecting human breathing rate from a non contact biosensor is considered in this paper. The method has low computational and RAM requirements making it well-suited to real-time, low power implementation on a microcontroller. Time and frequency domain methods are used to separate a 15s block of data into movement, breathing or absent states; a breathing rate estimate is then calculated. On a 1s basis, 96% of breaths were scored within 1 breath per minute of expert scored respiratory inductance plethysmography, while 99% of breaths were scored within 2 breaths per minute. When averaged over 30s, as is used in this respiration monitoring system, over 99% of breaths are within 1 breath per minute of the expert score.

A pilot study of the nocturnal respiration rates in COPD patients in the home environment using a non-contact biomotion sensor.

Nocturnal respiration rate parameters were collected from 20 COPD subjects over an 8 week period, to determine if changes in respiration rate were associated with exacerbations of COPD. These subjects were primarily GOLD Class 2 to 4, and had been recently discharged from hospital following a recent exacerbation. The respiration rates were collected using a non-contact radio-frequency biomotion sensor which senses respiratory effort and body movement using a short-range radio-frequency sensor. An adaptive notch filter was applied to the measured signal to determine respiratory rate over rolling 15 s segments. The accuracy of the algorithm was initially verified using ten manually-scored 15 min segments of respiration extracted from overnight polysomnograms. The calculated respiration rates were within 1 breath min(-1) for >98% of the estimates. For the 20 subjects monitored, 11 experienced one or more subsequent exacerbation of COPD (ECOPD) events during the 8 week monitoring period (19 events total). Analysis of the data revealed a significant increase in nocturnal respiration rate (e.g. >2 breath min(-1)) prior to many ECOPD events. Using a simple classifier of a change of 1 breath min(-1) in the mode of the nocturnal respiration rate, a predictive rule showed a sensitivity of 63% and specificity of 85% for predicting an exacerbation within a 5 d window. We conclude that it is possible to collect respiration rates reliably in the home environment, and that the respiration rate may be a potential indicator of change in clinical status.