SLEEP APNEA ASSESSMENT IN STROKE PATIENTS USING A SMARTPHONE

Sleep apnea is common but often underdiagnosed after stroke, hindering rehabilitation and functional recovery. Access to sleep studies remains limited for stroke patients, partly due to the inconvenience of current diagnostic tools. The widespread availability and built-in sensors of smartphones make them powerful solutions for mobile health applications, including sleep monitoring. This study aims to detect and evaluate sleep apnea in post-stroke patients using a smartphone and extract multimodal digital biomarkers to characterize their sleep patterns. Sleep tests were conducted on 20 patients with subacute ischemic stroke (11 men and 9 women, mean age 51±12 years) and 20 age- and sex-matched control subjects, using a smartphone-based system that recorded audio, accelerometer, and oximetry data. Signals were analyzed with custom-made automatic algorithms to compute respiratory, oxygenation, and sleep position indices. The apnea-hypopnea index (AHI) was significantly higher in the stroke than the control group (28±20 vs 15±10, p=0.01). Moderate-to-severe sleep apnea (AHI≥15) was present in 70% of stroke patients and 45% of control subjects. Stroke patients spent more time mouth breathing (23% vs 10%, p<0.001) and sleeping in supine position (67% vs 37%, p=0.004) than controls, contributing to upper airway obstruction. These findings obtained through novel multimodal digital biomarkers advance the understanding of post-stroke sleep apnea patterns and show the potential of smartphones as portable screening and monitoring tools. This approach can facilitate early sleep apnea detection after stroke, thereby reducing its substantial burden and improving rehabilitation outcomes.