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
{"title":"提出一种新的、微创的、软件智能手机设备来预测睡眠呼吸暂停及其严重程度。","authors":"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","doi":"10.1007/s11325-025-03441-w","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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><p><strong>Methods: </strong>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><p><strong>Results: </strong>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<sup>- 17</sup>), Pearson correlation 0.90 (p = 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 > 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<sup>- 11</sup>) and Pearson correlation is 0.87 (p = 1.7 × 10<sup>- 12</sup>).</p><p><strong>Conclusion: </strong>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><p><strong>Trial registration: </strong>NCT03803098.</p>","PeriodicalId":520777,"journal":{"name":"Sleep & breathing = Schlaf & Atmung","volume":"29 5","pages":"282"},"PeriodicalIF":2.0000,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12413423/pdf/","citationCount":"0","resultStr":"{\"title\":\"Proposition of a new, minimally-invasive, software smartphone device to predict sleep apnea and its severity.\",\"authors\":\"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\",\"doi\":\"10.1007/s11325-025-03441-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>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><p><strong>Methods: </strong>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><p><strong>Results: </strong>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<sup>- 17</sup>), Pearson correlation 0.90 (p = 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 > 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<sup>- 11</sup>) and Pearson correlation is 0.87 (p = 1.7 × 10<sup>- 12</sup>).</p><p><strong>Conclusion: </strong>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><p><strong>Trial registration: </strong>NCT03803098.</p>\",\"PeriodicalId\":520777,\"journal\":{\"name\":\"Sleep & breathing = Schlaf & Atmung\",\"volume\":\"29 5\",\"pages\":\"282\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12413423/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sleep & breathing = Schlaf & Atmung\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s11325-025-03441-w\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sleep & breathing = Schlaf & Atmung","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s11325-025-03441-w","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Proposition of a new, minimally-invasive, software smartphone device to predict sleep apnea and its severity.
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.
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