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

Abstract

Purpose: 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).

Methods: 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.

Results: 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}).

Conclusion: 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.

Trial registration: NCT03803098.

Keywords: Artificial intelligence; Deep learning; Diagnosis; Sleep apnea; Smartphone.

Fig. 1

full installation of a smartphone and the PSG on a patient

Fig. 2

Illustration of various respiratory events: central apnea, obstructive hypopnea, and obstructive apnea (from left to right), with, in parallel, some of the Apneal^® signals used during manual scoring (top) and signals extracted from the PSG (bottom)

Fig. 3

Explanation of our data processing pipeline

Fig. 4

Results of the manual scoring. (A) Confusion matrix between the SAHS severities obtained from the PSG AHI and the Manual Apneal^® AHI. (B) Regression plot between the PSG AHI and theManual Apneal^® AHI. (C) Bland Altman plot between the PSG AHI and the Manual Apneal^® AHI

Fig. 5

Results of the manual scoring from certified scorers. (A) Confusion matrix between the SAHS severities obtained from the PSG AHI and the Manual certified Apneal^® AHI. (B) Regression plot between the PSG AHI and the Manual certified Apneal^® AHI. (C) Bland Altman plot between the PSG AHI and the Manual certified Apneal^® AHI

Fig. 6

Results of the automatic scoring. (A) Confusion matrix between the SAHS severities obtained from the PSG-second AHI and the automatic Apneal^® AHI, using the severity threshold of IAH (< 5, 5–15 and > 15/h). (B) Regression plot between the PSG-second AHI and the automatic Apneal^® AHI. (C) Bland Altman plot between the PSG-second AHI and the automatic Apneal^® AHI