SUMMARYWe studied a novel non-contact biomotion sensor, which has been developed for identifying sleep ⁄ wake patterns in adult humans. The biomotion sensor uses ultra lowpower 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.
Study Objectives: To assess the sleep detection and staging validity of a non-contact, commercially available bedside bio-motion sensing device (S+, ResMed) and evaluate the impact of algorithm updates. Methods: Polysomnography data from 27 healthy adult participants was compared epoch-by-epoch to synchronized data that were recorded and staged by actigraphy and S+. An update to the S+ algorithm (common in the rapidly evolving commercial sleep tracker industry) permitted comparison of the original (S+V1) and updated (S+V2) versions. Results: Sleep detection accuracy by S+V1 (93.3%), S+V2 (93.8%), and actigraphy (96.0%) was high; wake detection accuracy by each (69.6%, 73.1%, and 47.9%, respectively) was low. Higher overall S+ specificity, compared to actigraphy, was driven by higher accuracy in detecting wake before sleep onset (WBSO), which differed between S+V2 (90.4%) and actigraphy (46.5%). Stage detection accuracy by the S+ did not exceed 67.6% (for stage N2 sleep, by S+V2) for any stage. Performance is compared to previously established variance in polysomnography scored by humans: a performance standard which commercial devices should ideally strive to reach. Conclusions: Similar limitations in detecting wake after sleep onset (WASO) were found for the S+ as have been previously reported for actigraphy and other commercial sleep tracking devices. S+ WBSO detection was higher than actigraphy, and S+V2 algorithm further improved WASO accuracy. Researchers and clinicians should remain aware of the potential for algorithm updates to impact validity.
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/m(2)). SDB severity ranged from subjects free of SDB to those with severe SDB (AHI: 0.8-96 events/hours). SleepMinder's 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%.
SUMMARYObstructive 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 À1 ) 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.
SUMMARYAmbulatory 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 AE 14 years and apnoea-hypopnoea index 21 AE 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.
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