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Purpose <p>obstructive sleep apnea is underdiagnosed due to limited access to polysomnography (PSG). We aimed to assess the performances of Apneal<sup>®</sup>, an application recording sound and movements thanks to a smartphone’s microphone, accelerometer and gyroscope, to estimate patients’ apnea-hypopnea index (AHI).</p> Methods <p>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<sup>®</sup> automatic scoring of respiratory events, end 2022), in adult patients.</p> Results <p>46 patients (women 34%, BMI 28.7&#xa0;kg/m²) were included. Sensitivity of manual scoring was 0.91 (95% CI [0.8-1]) for IAH &gt; 15 and 0.85 [0.67-1] for AHI &gt; 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 &gt; 15, and AUC-ROC of 0.95 [0.860.99] and AUC-PR of 0.93 [0.81–0.99] for AHI &gt; 30. The ICC between the AHI estimated manually, and from the PSG is 0.89 (<i>p</i> = 6.7 × 10<sup>− 17</sup>), Pearson correlation 0.90 (<i>p</i> = 1.25 × 10<sup>− 17</sup>). Automatic scoring found sensitivity of 1 [0.95-1], PPV of 0.9 [0.8–0.9] for AHI &gt; 15, and sensitivity 0.95 [0.84-1], PPV 0.69 [0.52–0.85] for AHI &gt; 30. The ICC between the estimated AHI, and PSG scorings is 0.84 (<i>p</i> = 5.4 × 10<sup>− 11</sup>) and Pearson correlation is 0.87 (<i>p</i> = 1.7 × 10<sup>− 12</sup>).</p> Conclusion <p>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.</p> Trial registration <p>NCT03803098.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月5日

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  • Justine Frija,
  • Juliette Millet,
  • Emilie Béquignon,
  • Ala Covali,
  • Guillaume Cathelain,
  • Josselin Houenou,
  • Hélène Benzaquen,
  • Pierre A. Geoffroy,
  • Emmanuel Bacry,
  • Mathieu Grajoszex,
  • Marie-Pia d’Ortho

摘要

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.