  Obstructive sleep apnea (OSA) is frequent and responsible for cardiovascular
complications and excessive daytime sleepiness. It is underdiagnosed due to the
difficulty to access the gold standard for diagnosis, polysomnography (PSG).
Alternative methods using smartphone sensors could be useful to increase
diagnosis. The objective is to assess the performances of Apneal, an
application that records the sound using a smartphone's microphone and
movements thanks to a smartphone's accelerometer and gyroscope, to estimate
patients' AHI. In this article, we perform a monocentric proof-of-concept study
with a first manual scoring step, and then an automatic detection of
respiratory events from the recorded signals using a sequential deep-learning
model which was released internally at Apneal at the end of 2022 (version 0.1
of Apneal automatic scoring of respiratory events), in adult patients during
in-hospital polysomnography.46 patients (women 34 per cent, mean BMI 28.7 kg
per m2) were included. For AHI superior to 15, sensitivity of manual scoring
was 0.91, and positive predictive value (PPV) 0.89. For AHI superior to 30,
sensitivity was 0.85, PPV 0.94. We obtained an AUC-ROC of 0.85 and an AUC-PR of
0.94 for the identification of AHI superior to 15, and AUC-ROC of 0.95 and
AUC-PR of 0.93 for AHI superior to 30. Promising results are obtained for the
automatic annotations of events.This article shows that 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. A larger multicentric validation study, involving subjects
with different SAHS severity is required to confirm these results.
