An Internet of Medical Things system to increase continuous positive airway pressure usage in patients with sleep disordered breathing

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Angelo Croatti, Sara Montagna, Carolina Lombardi, Gianfranco Parati, Martino F. Pengo, Alessandro Silvani
SN Computer Science 2(117)

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder and it is associated with increased daytime sleepiness and cardiovascular risk. Continuous positive airway pressure (CPAP) requiring a pressure-generating device connected via tubing to a mask during sleep is an effective treatment, but compliance is often sub-optimal. Behavioural interventions are effective in improving adherence to CPAP. We aimed to provide proof of principle for the operation of a low-cost, self-standing, internet-based system to measure and promote CPAP compliance. The system is composed by triaxial acceleration sensors attached to the CPAP mask and to the wrist, able to record CPAP usage information, and a mobile app that collects such information and, thorough a chatbot, feeds back to the patient to improve treatment compliance. The mask subsystem identifies time periods when the mask is put on based on relatively high values of the ratio between acceleration spectral power at frequencies < 0.33 Hz vs. 0.33-2 Hz, over 1-minute windows. Accuracy in identification may be increased taking account of the surges in the standard deviation of wrist accelerations over 1-minute windows that accompany putting on and taking off the mask. The whole system can represent a unique tool capable to monitor and improve patients' adherence to CPAP treatment, thus improving sleep quality and OSA related symptoms. Its main strength lies in its simplicity, low-cost, and independence from the specific CPAP device and mask employed.

journal or series
book SN Computer Science (SNCS)