Validation of a new, minimally-invasive, software smartphone device to predict sleep apnea and its severity: transversal study

• URL: http://arxiv.org/abs/2406.16953v1
• Date: Thu, 20 Jun 2024 14:36:15 GMT
• Title: Validation of a new, minimally-invasive, software smartphone device to predict sleep apnea and its severity: transversal study
• Authors: Justine Frija, Juliette Millet, Emilie Bequignon, Ala Covali, Guillaume Cathelain, Josselin Houenou, Helene Benzaquen, Pierre Alexis Geoffroy, Emmanuel Bacry, Mathieu Grajoszex, Marie-Pia d Ortho,
• Abstract summary: Obstructive sleep apnea (OSA) is frequent and responsible for cardiovascular complications and excessive daytime sleepiness. Alternative methods using smartphone sensors could be useful to increase diagnosis. This article shows that manual scoring of smartphone-based signals is possible and accurate compared to PSG-based scorings.
• Score: 3.798946451618375
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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