Unobtrusive Screening of Central Sleep Apnea From Pressure Sensors Measurements: A Patient-Specific Longitudinal Study

Abstract

Historically, the lack of patients’ sleep histories has caused low identification of sleep apnea (SA) and referral rates. Moreover, the costly and time-consuming nature of polysomnography (PSG) as a standard clinical test for detecting SA and the lack of sleep clinics has created a demand for suitable home-based monitoring devices. Pressure measurement using a pressure sensitive mat (PSM) can address the challenges found in current sleep-monitoring solutions. The noncontact PSM has a potential to replace obtrusive breathing sensors in the sleep lab and to be used as a prescreening tool for patients suspected of having SA. Applying classical support vector machine (SVM), this article presents a personalized system based on the measurements of each patient to detect central SA (CSA) events and monitor sleep characteristics longitudinally. For this purpose, sensor set-ups were installed in nine seniors’ homes to collect unsupervised pressure data in approximately one year ranging from 8 to 12 months. Cost-based and resampling-based approaches were examined to combat imbalanced data. The results showed that the cost-based method outperformed other methods. Next, the patient-specific system was used to determine the total number of CSA events, as well as their starting time and duration in each day. The SA severity was measured by the central apnea index (CAI). In addition, other sleep characteristics such as bed occupancy (BO), day clock, and night clock were extracted from the PSM measurements. The impact of longitudinal sleep monitoring could be in tracking SA treatment progression, and possibly providing information on the interaction between SA and other disease progressions.