Proposition of a new, minimally-invasive, software smartphone device to predict sleep apnea and its severity

Résumé fourni par la source

obstructive sleep apnea is underdiagnosed due to limited access to polysomnography (PSG). We aimed to assess the performances of Apneal ® , an application recording sound and movements thanks to a smartphone’s microphone, accelerometer and gyroscope, to estimate patients’ apnea-hypopnea index (AHI). monocentric proof-of-concept study with a first manual scoring step, then automatic detection of respiratory events from recorded signals using a sequential deep-learning model (version 0.1 of Apneal ® automatic scoring of respiratory events, end 2022), in adult patients. 46 patients (women 34%, BMI 28.7 kg/m²) were included. Sensitivity of manual scoring was 0.91 (95% CI [0.8-1]) for IAH > 15 and 0.85 [0.67-1] for AHI > 30, and positive predictive values (PPV) 0.89 [0.76–0.97] and 0.94 [0.8-1]. We obtained an AUC-ROC of 0.85 (95% CI [0.69–0.96]) and AUC-PR of 0.94 (95% CI [0.84–0.99]) for the identification of AHI > 15, and AUC-ROC of 0.95 [0.860.99] and AUC-PR of 0.93 [0.81–0.99] for AHI > 30. The ICC between the AHI estimated manually, and from the PSG is 0.89 ( p = 6.7 × 10 − 17 ), Pearson correlation 0.90 ( p = 1.25 × 10 − 17 ). Automatic scoring found sensitivity of 1 [0.95-1], PPV of 0.9 [0.8–0.9] for AHI > 15, and sensitivity 0.95 [0.84-1], PPV 0.69 [0.52–0.85] for AHI > 30. The ICC between the estimated AHI, and PSG scorings is 0.84 ( p = 5.4 × 10 − 11 ) and Pearson correlation is 0.87 ( p = 1.7 × 10 − 12 ). Manual scoring of smartphone-based signals is possible and accurate compared to PSG-based scorings. Automatic scoring method based on a deep learning model provides promising results. NCT03803098.

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