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).

Informations de copyright

2020 Journal of Thoracic Disease. All rights reserved.

Déclaration de conflit d'intérêts

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/jtd-20-804). The series “Sleep Section” was commissioned by the editorial office without any funding or sponsorship. RS and SMcM have patents WO/2020/104465-Methods and Apparatus for Detection of Disordered Breathing, WO/2019/122412-Apparatus, System, And Method for Health And Medical Sensing, WO/2019/122414-Apparatus, System, and Method for Physiological Sensing In Vehicles, US 201662396616 Apparatus, System, And Method for Detecting Physiological Movement from Audio and Multimodal Signals, and WO2019122413-Apparatus, System, and Method for Motion Sensing pending. RT and GL have patents WO/2020/104465 and US 201662396616 pending. MW has a patent WO/2019/122412 and WO2019122413 pending. NF and NOM have patent US 201662396616 pending. IF reports grants from Löwenstein, grants from Philips, personal fees from ResMed, outside the submitted work. TP reports grants from ResMed, during the conduct of the study; grants from ResMed, grants and personal fees from Philips, grants and personal fees from Löwenstein Medical, personal fees from Jazz Pharma, personal fees from Heel Pharma, grants from Itamar Medical, other from Bayer Healthcare, outside the submitted work; and Shareholder with Advanced Sleep Research GmbH, Somnico GmbH, and The Siestagroup GmbH. The authors have no other conflicts of interest to declare.