BodySleep Algorithm for OSA Diagnosis in Children

Diagnosing obstructive sleep apnea-hypopnea syndrome in children (OSA) requires the performance of polysomnography (PSG) in the hospital which is sometimes challenging to perform in children, and time-consuming for installation and analysis. Simplified recording and analysis methods are preferable in children but require validation in this population. The BodySleep automatic algorithm of the polysomnograph used in our lab (A1-Nox, ResMed) associated only with respiratory signals could be used to identify respiratory events. Thus the child would have fewer sensors installed on him.

The diagnosis of obstructive sleep apnea-hypopnea syndrome in children (OSA) requires the performance of polysomnography (PSG) in the hospital with video surveillance and monitoring by a nurse to put the sensors back on the child if necessary. During the night. The PSG gives the index of obstructive apnea-hypopnea (IAHO) necessary for the diagnosis of OSAS and to determine its severity. But the PSG is a rather cumbersome examination, sometimes challenging to perform in children, with several sensors and electrodes to install (electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG), necessary to determine the periods of wakefulness -sleep and intra-sleep micro-arousals, nasal cannula, thoracoabdominal straps, pulse oximetry, actimetry to score respiratory events), time-consuming for installation and analysis. Simplified recording and analysis methods are preferable in children but require validation in this population. The BodySleep automatic algorithm of the polysomnograph used in our service (A1-Nox, ResMed) combines actigraphy data (body position during sleep) and induction plethysmography signal resulting from the thoracoabdominal belts to identify sleep-wake stages could be used instead of EEG, EOG and EMG electrodes. The BodySleep algorithm associated only with respiratory signals (nasal cannula, thoracoabdominal straps, pulse oximetry, actimetry) could be used to identify respiratory events. Thus the child would have fewer sensors installed on him. The hypothesis of this study is that the BodySleep algorithm associated with respiratory signals can identify OSA in children.