Abstract
The gold standard for assessing sleep apnea, polysomnography, is resource intensive and inconvenient. Thus, several simpler alternatives have been proposed. However, validations of these alternatives have focused primarily on estimating the apnea‐hypopnea index (apnea events per hour of sleep), which means information, clearly important from a physiological point of view such as apnea type, apnea duration, and temporal distribution of events, is lost. The purpose of the present study was to investigate if this information could also be provided with the combination of radar technology and pulse oximetry by classifying sleep apnea events on a second‐by‐second basis. Fourteen patients referred to home sleep apnea testing by their medical doctor were enrolled in the study (6 controls and 8 patients with sleep apnea; 4 mild, 2 moderate, and 2 severe) and monitored by Somnofy (radar‐based sleep monitor) in parallel with respiratory polygraphy. A neural network was trained on data from Somnofy and pulse oximetry against the polygraphy scorings using leave‐one‐subject‐out cross‐validation. Cohen’s kappa for second‐by‐second classifications of no event/event was 0.81, or almost perfect agreement. For classifying no event/hypopnea/apnea and no event/hypopnea/obstructive apnea/central apnea/mixed apnea, Cohen’s kappa was 0.43 (moderate agreement) and 0.36 (fair agreement), respectively. The Bland‐Altman 95% limits of agreement for the respiratory event index (apnea events per hour of recording) were ‐8.25 and 7.47, and all participants were correctly classified in terms of sleep apnea severity. Furthermore, the results showed that the combination of radar and pulse oximetry could be more accurate than the two technologies separately. Overall, the results indicate that radar technology and pulse oximetry could reliably provide information on a second‐by‐second basis for no event/event which could be valuable for management of sleep apnea. To be clinically useful, a larger study is necessary to validate the algorithm on a general population.
Highlights
Sleep apnea is characterized by repetitive reduction or cessation of airflow during sleep resulting in microarousals and is associated with increased risk of daytime sleepiness, coronary artery disease, stroke, and early death [1]
The amplitude is clearly reduced during apnea events
The results in the present paper indicate that the combination of radar technology and pulse oximetry can classify sleep apnea more accurately than the two technologies separately
Summary
Sleep apnea is characterized by repetitive reduction or cessation of airflow during sleep resulting in microarousals and is associated with increased risk of daytime sleepiness, coronary artery disease, stroke, and early death [1]. Despite being a serious disease, sleep apnea is underrecognized and underdiagnosed [2]. The gold standard for diagnosing sleep apnea is inlaboratory polysomnography (PSG) [3]. PSG uses a comprehensive set of sensors to measure brain, muscular, respiratory, and cardiovascular activity, and collected data is manually analyzed by a sleep specialist. While PSG is accurate, it is resource intensive and can be inconvenient for the patient, who must sleep with several sensors attached to their body. Overnight respiratory polygraphy (RP), which in contrast to PSG does not measure brain activity, is often used as a simpler alternative when diagnosing sleep apnea. RP is still resource intensive and inconvenient for the patient. To reduce the amount of manual work required, PSG and RP software have been enhanced with algorithms
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
