Abstract
Polysomnography systems used for the diagnosis of sleep respiratory disorders are comprised of multiple sensors, including abdomen and thorax respiratory inductance plethysmography (RIP) belts to record respiratory effort. However, RIP belts are known to be susceptible to signal loss. To resolve this, we utilized a contactless three-dimensional (3D) time-of-flight (TOF) camera to monitor respiratory effort. We aimed to show that respiratory effort monitoring can be achieved by 3D TOF camera recording instead of RIP belts. The use of RIP belt signals is twofold. Firstly, the signals are used to classify the apnea events into obstructive, central, and mixed. Additionally, the American Academy of Sleep Medicine (AASM) Scoring Manual recommends the scoring of apneas and hypopneas using RIPSum (the sum of the abdomen and thorax RIP signals) when the airflow sensors are unavailable. We therefore used the 3D effort signal to classify apneas and compared it to the RIP signal classification. Reduced effort events from RIP and 3D effort signals were compared to the apnea and hypopnea events. Furthermore, the changes in effort during the events were compared between the two effort signals. Classification by 3D effort signal performed well, with 80% accuracy. It worked best for central apneas, with an accuracy of 99%. There was a high correlation of r = 0.88 (r ≠ 0, p = 0.0001) between the 3D effort signal events and RIPSum events. There was also a significant correlation of 0.62 (r ≠ 0, p = 0.0001) between 3D effort signal and RIPSum in the decrease of effort during apnea and hypopnea events. We conclude that respiratory effort derived from a 3D TOF camera can be used as an alternative to RIP belts for scoring of apneas and hypopneas and classification of apneas.
Highlights
Polysomnography systems used for the diagnosis of sleep respiratory disorders are comprised of multiple sensors, including abdomen and thorax respiratory inductance plethysmography (RIP) belts to record respiratory effort
A total of 618 apneas were classified by the thorax and abdomen RIP belts by visual inspection using the Academy of Sleep Medicine (AASM) rules, serving as the ground truth
An example of RespEffort3D showing an absence of respiratory effort during a central apnea event can be found in
Summary
Polysomnography systems used for the diagnosis of sleep respiratory disorders are comprised of multiple sensors, including abdomen and thorax respiratory inductance plethysmography (RIP) belts to record respiratory effort. We aimed to show that respiratory effort monitoring can be achieved by 3D TOF camera recording instead of RIP belts. The signals are used to classify the apnea events into obstructive, central, and mixed. The American Academy of Sleep Medicine (AASM) Scoring Manual recommends the scoring of apneas and hypopneas using RIPSum (the sum of the abdomen and thorax RIP signals) when the airflow sensors are unavailable. We used the 3D effort signal to classify apneas and compared it to the RIP signal classification. Reduced effort events from RIP and 3D effort signals were compared to the apnea and hypopnea events. Classification by 3D effort signal performed well, with 80% accuracy. It worked best for central apneas, with an accuracy of
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