Ambulatory detection of sleep apnea using a non-contact biomotion sensor.

The high prevalence of obstructive sleep apnea has led to increasing interest in ambulatory diagnosis. The SleepMinder™ (SM) is a novel non-contact device that employs radiofrequency wave technology to assess the breathing pattern, and thereby estimate obstructive sleep apnea severity. We assessed the performance of SleepMinder™ in the home diagnosis of obstructive sleep apnea. One-hundred and twenty-two subjects were prospectively recruited in two protocols, one from an unselected sleep clinic cohort (n = 67, mean age 51 years) and a second from a hypertension clinic cohort (n = 55, mean age 58 years). All underwent 7 consecutive nights of home monitoring (SMHOME ) with the SleepMinder™ as well as inpatient-attended polysomnography in the sleep clinic cohort or cardiorespiratory polygraphy in the hypertension clinic cohort with simultaneous SleepMinder™ recordings (SMLAB ). In the sleep clinic cohort, median SMHOME apnea-hypopnea index correlated significantly with polysomnography apnea-hypopnea index (r = .68; p < .001), and in the hypertension clinic cohort with polygraphy apnea-hypopnea index (r = .7; p < .001). The median SMHOME performance against polysomnography in the sleep clinic cohort showed a sensitivity and specificity of 72% and 94% for apnea-hypopnea index ≥ 15. Device performance was inferior in females. In the hypertension clinic cohort, SMHOME showed a 50% sensitivity and 72% specificity for apnea-hypopnea index ≥ 15. SleepMinder™ classified 92% of cases correctly or within one severity class of the polygraphy classification. Night-to-night variability in home testing was relatively high, especially at lower apnea-hypopnea index levels. We conclude that the SleepMinder™ device provides a useful ambulatory screening tool, especially in a population suspected of obstructive sleep apnea, and is most accurate in moderate-severe obstructive sleep apnea.

Screening for obstructive sleep apnea with novel hybrid acoustic smartphone app technology.

BACKGROUND: Obstructive sleep apnea (OSA) has a high prevalence, with an estimated 425 million adults with apnea hypopnea index (AHI) of ≥15 events/hour, and is significantly underdiagnosed. This presents a significant pain point for both the sufferers, and for healthcare systems, particularly in a post COVID-19 pandemic world. As such, it presents an opportunity for new technologies that can enable screening in both developing and developed countries. In this work, the performance of a non-contact OSA screener App that can run on both Apple and Android smartphones is presented. METHODS: The subtle breathing patterns of a person in bed can be measured via a smartphone using the "Firefly" app technology platform [and underpinning software development kit (SDK)], which utilizes advanced digital signal processing (DSP) technology and artificial intelligence (AI) algorithms to identify detailed sleep stages, respiration rate, snoring, and OSA patterns. The smartphone is simply placed adjacent to the subject, such as on a bedside table, night stand or shelf, during the sleep session. The system was trained on a set of 128 overnights recorded at a sleep laboratory, where volunteers underwent simultaneous full polysomnography (PSG), and "Firefly" smartphone app analysis. A separate independent test set of 120 recordings was collected across a range of Apple iOS and Android smartphones, and withheld for performance evaluation by a different team. An operating point tuned for mid-sensitivity (i.e., balancing sensitivity and specificity) was chosen for the screener. RESULTS: The performance on the test set is comparable to ambulatory OSA screeners, and other smartphone screening apps, with a sensitivity of 88.3% and specificity of 80.0% [with receiver operating characteristic (ROC) area under the curve (AUC) of 0.92], for a clinical threshold for the AHI of ≥15 events/hour of detected sleep time. CONCLUSIONS: The "Firefly" app based sensing technology offers the potential to significantly lower the barrier of entry to OSA screening, as no hardware (other than the user's personal smartphone) is required. Additionally, multi-night analysis is possible in the home environment, without requiring the wearing of a portable PSG or other home sleep test (HST).

Sleep Staging Monitoring Based on Sonar Smartphone Technology.

This paper presents the validation results of a new non-contact ultrasonic technology, which employs inaudible Sonar to monitor the movements and respiration of a subject in bed. Sleep monitoring can be achieved by placing a smartphone onto the bedside table and starting a custom app. The app employs sophisticated and novel proprietary algorithms to identify sleep stages: Wake (W), Light Sleep (N1, N2 sleep), Deep Sleep (N3 sleep), Rapid Eye Movement (REM) Sleep or Absence.The sleep staging performance of the app were assessed by testing it against expert manually scored polysomnography (PSG) of 38 subjects gathered in a sleep laboratory. As a secondary assessment, on the same dataset, the performance of the app is compared to that of a reference non-contact device, the S+ by ResMed.Performance across different sleep stage detections was balanced, exceeding the agreement typically reported for actigraphy based devices [1], [2] thanks to a significantly higher sensitivity for all sleep stages. Furthermore, the performance of the app was found to be comparable to the S+ by ResMed product [3], [4].The combination of unobtrusive non-contact sensing and accurate sleep quality assessment, coupled with removal of the requirement to purchase a custom device to enable monitoring of sleep, enables consumers to measure their sleep in the home environment in a zero-cost and accessible manner, while providing sleep staging information not otherwise available with actigraphy based devices.

Detection of Sleep Apnea Using Sonar Smartphone Technology.

This paper assesses the performance of a new noncontact sensing system based on Sonar technology as a Sleep Disordered Breathing (SDB) screener. The respiration and movements of a subject in bed can be measured via a smartphone placed onto a bedside table equipped with a custom app. The app employs novel proprietary algorithms to identify sleep stages and detect SDB patterns.The SDB screener was trained on a set of 94 overnights recorded at a sleep laboratory, where volunteers underwent simultaneous monitoring via a full polysomnography (PSG) system and a smartphone equipped with the app. An additional fully independent set of 68 recordings, uniformly distributed across SDB severity classes, were held out for independent testing. The performance on the test set is excellent and comparable to other existing ambulatory SDB screeners, with a sensitivity of 94% and specificity of 97%, for a clinical threshold for the Apnea Hypopnea Index (AHI) of 15 events/hour.The technology can easily be adopted to scale, as no purchase of dedicated sensors is needed, providing a much needed low- cost alternative for monitoring and potentially screening of large population segments. Furthermore, the non-invasive, contactless sensing does not interfere with the sleeping habits of the user, facilitating longitudinal assessment. This, in combination with the simultaneous measurement of the user's sleep quality, could provide invaluable insights in the subject's response to SDB therapy and lead to increased patient adherence.

Sleep Validity of a Non-Contact Bedside Movement and Respiration-Sensing Device.

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. COMMENTARY: A commentary on this article appears in this issue on page 935.