On the use of smartphones for detecting obstructive sleep apnea (original) (raw)
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BREATHMONITOR: SLEEP APNEA MOBILE DETECTOR
IEEE, 2020
This paper describes the on-line AI system for diagnosis and monitoring sleep apnea at home, based on the processing of human respiratory signals from an accelerometer and pressure transducer composition by using Deep machine learning and alternative louder analytics
New technology to assess sleep apnea: wearables, smartphones, and accessories
F1000Research, 2018
Sleep medicine has been an expanding discipline during the last few decades. The prevalence of sleep disorders is increasing, and sleep centers are expanding in hospitals and in the private care environment to meet the demands. Sleep medicine has evidence-based guidelines for the diagnosis and treatment of sleep disorders. However, the number of sleep centers and caregivers in this area is not sufficient. Many new methods for recording sleep and diagnosing sleep disorders have been developed. Many sleep disorders are chronic conditions and require continuous treatment and monitoring of therapy success. Cost-efficient technologies for the initial diagnosis and for follow-up monitoring of treatment are important. It is precisely here that telemedicine technologies can meet the demands of diagnosis and therapy follow-up studies. Wireless recording of sleep and related biosignals allows diagnostic tools and therapy follow-up to be widely and remotely available. Moreover, sleep research ...
Sleep medicine has been an expanding discipline during the last few decades. The prevalence of sleep disorders is increasing, and sleep centers are expanding in hospitals and in the private care environment to meet the demands. Sleep medicine has evidence-based guidelines for the diagnosis and treatment of sleep disorders. However, the number of sleep centers and caregivers in this area is not sufficient. Many new methods for recording sleep and diagnosing sleep disorders have been developed. Many sleep disorders are chronic conditions and require continuous treatment and monitoring of therapy success. Cost-efficient technologies for the initial diagnosis and for follow-up monitoring of treatment are important. It is precisely here that telemedicine technologies can meet the demands of diagnosis and therapy follow-up studies. Wireless recording of sleep and related biosignals allows diagnostic tools and therapy follow-up to be widely and remotely available. Moreover, sleep research requires new technologies to investigate underlying mechanisms in the regulation of sleep in order to better understand the pathophysiology of sleep disorders. Home recording and non-obtrusive recording over extended periods of time with telemedicine methods support this research. Telemedicine allows recording with little subject interference under normal and experimental life conditions.
Performing detection and real-time monitoring of Obstructive Sleep Apnea (OSA) is a significant healthcare task. An easy, cheap, and mobile approach to monitor patients with OSA is proposed here. It gathers data from a patient by a single- channel ECG, and offline automatically extracts knowledge about that patient as a set of IF...THEN rules containing Heart Rate Variability (HRV) parameters. These rules are then used in the real-time mobile monitoring system: ECG data is collected by a wearable sensor, sent to a mobile device, and processed online to compute HRV-related parameter values. If a rule is activated by those values, the system produces an alarm. A literature database of OSA patients has been used to test the approach.
Scientific Reports
Obstructive sleep apnea (OSA) is extremely common and has several consequences. However, most cases remain undiagnosed. One limitation is the lack of simple and validated methods for OSA diagnosis at home. The aim of this study was to validate a wireless high-resolution oximeter with a built-in accelerometer linked to a smartphone with automated cloud analysis (Biologix) that was compared with a home sleep test (HST, Apnea Link Air) performed on the same night. We recruited 670 patients out of a task force of 1013 patients with suspected OSA who were referred to our center for diagnosis. The final sample consisted of 478 patients (mean age: 56.7 ± 13.1 years, mean body mass index: 31.9 ± 6.3 kg/m2). To estimate the night-to-night OSA severity variability, 62 patients underwent HST for two consecutive nights. The HST-apnea–hypopnea index (AHI) and the Biologix-oxygen desaturation index (ODI) was 25.0 ± 25.0 events/h and 24.9 ± 26.5 events/h, respectively. The area under the curve—sen...