AbstractThe aim of this study was to build and validate an artificial neural network (ANN) algorithm to predict sleep using data from a portable monitor (Biologix system) consisting of a high-resolution oximeter with built-in accelerometer plus smartphone application with snoring recording and cloud analysis.

摘要本研究的目的是建立和验证一种人工神经网络（ANN）算法，使用便携式监护仪（Biologix系统）的数据预测睡眠，该监护仪由高分辨率血氧仪和内置加速计以及智能手机应用程序组成，具有打鼾记录和云分析。

A total of 268 patients with suspected obstructive sleep apnea (OSA) were submitted to standard polysomnography (PSG) with simultaneous Biologix (age: \(56\pm 11\) years; body mass index: \(30.9\pm 4.6\)\(\hbox {kg/m}^{2}\), apnea-hypopnea index [AHI]: \(35\pm 30\) events/h). Biologix channels were input features for construction an ANN model to predict sleep.

共有268名疑似阻塞性睡眠呼吸暂停（OSA）患者接受了标准多导睡眠图（PSG），同时进行了Biologix（年龄：\（56 \ pm 11\）年；体重指数：\（30.9 pm 4.6）\（\ hbox{kg/m}^{2}），呼吸暂停低通气指数[AHI]：\（35 pm 30）事件/小时）。Biologix通道是用于构建预测睡眠的ANN模型的输入特征。

A k-fold cross-validation method (k=10) was applied, ensuring that all sleep studies (N=268; 246,265 epochs) were included in both training and testing across all iterations. The final ANN model, evaluated as the mean performance across all folds, resulted in a sensitivity, specificity and accuracy of 91.5%, 71.0% and 86.1%, respectively, for detecting sleep.

应用k倍交叉验证方法（k=10），确保所有睡眠研究（N=268；246265个时期）都包括在所有迭代的训练和测试中。最终的ANN模型被评估为所有折叠的平均性能，用于检测睡眠的敏感性，特异性和准确性分别为91.5%，71.0%和86.1%。

As compared to the oxygen desaturation index (ODI) from Biologix without sleep prediction, the bias (mean difference) between PSG-AHI and Biologix-ODI with sleep prediction (Biologix-Sleep-ODI) decreased significantly (3.40 vs. 1.02 events/h, p<0.001). We conclude that sleep prediction by an ANN model using data from oximeter, accelerometer, and snoring is accurate and improves Biologix system OSA diagnostic precision..

与没有睡眠预测的Biologix的氧饱和度指数（ODI）相比，PSG-AHI和具有睡眠预测的Biologix ODI（Biologix sleep ODI）之间的偏差（平均差异）显着降低（3.40 vs.1.02事件/小时，p<0.001）。我们得出的结论是，使用血氧仪，加速度计和打鼾的数据通过ANN模型进行的睡眠预测是准确的，并提高了Biologix系统OSA的诊断精度。。

IntroductionObstructive sleep apnea (OSA) is characterized by repetitive episodes of upper airway obstruction, resulting in sleep fragmentation and oxygen desaturation1. OSA is associated with several health consequences, including poor sleep quality, excessive daytime sleepiness, and increased cardiovascular risk2.

引言阻塞性睡眠呼吸暂停（OSA）的特征是上呼吸道阻塞的重复发作，导致睡眠破碎和氧饱和度降低1。OSA与几种健康后果有关，包括睡眠质量差，白天过度嗜睡和心血管风险增加2。

Polysomnography (PSG) is considered the gold standard method for OSA diagnosis3. However, PSG is expensive and inconvenient for patients3. Portable monitoring (PM) is a simplified method that has been validated for OSA diagnosis4. In contrast to PSG, PM does not detect sleep. The consequence of this limitation is that the number of respiratory events in PM devices are reported by hour of monitoring rather than hours of sleep.

多导睡眠图（PSG）被认为是OSA诊断的金标准方法3。然而，PSG对患者来说既昂贵又不方便3。便携式监测（PM）是一种简化的方法，已被验证用于OSA诊断4。与PSG相反，PM不会检测到睡眠。这种限制的结果是PM设备中的呼吸事件数量是按监测小时而不是睡眠小时报告的。

Therefore, the absence of sleep monitoring is a potential source of variability between PSG and PM. Biologix system is a new PM device based on a high-resolution wireless oximeter (\(\hbox {Oxistar}^{\textrm{TM}}\), Biologix Sistemas S.A., Brazil) with built-in accelerometer and a smartphone application (app) that is downloaded to the patient’s smartphone.

因此，缺乏睡眠监测是PSG和PM之间变异的潜在来源。Biologix系统是一种基于高分辨率无线血氧仪（\（\ hbox{Oxistar}^{\ textrm{TM}}\），Biologix Sistemas S.a.，巴西）的新型PM设备，带有内置加速度计和下载到患者智能手机上的智能手机应用程序（app）。

The app records snoring, and all information is automatically processed in the cloud. Biologix system has been validated for OSA diagnosis against PSG in the sleep laboratory5 and against traditional PM at home6. However, Biologix system does not monitor sleep and therefore reports oxygen desaturation index (ODI) based on hours of monitoring rather than hours of sleep.

该应用程序记录打鼾，所有信息都在云中自动处理。Biologix系统已在睡眠实验室5中针对PSG进行OSA诊断，并在家中针对传统PM进行了验证6。然而，Biologix系统不监测睡眠，因此根据监测时间而不是睡眠时间报告氧饱和度指数（ODI）。

Therefore, the objective of this study was to build and validate an artificial neural network (ANN) algorithm using data from oximeter, accelerometer and snoring to detect sleep. We also tested the hypothesis that ANN model improves the Biologix system OSA diagnostic precision.MethodsPatients and data collectionThe stud.

因此，本研究的目的是使用血氧计，加速度计和打鼾的数据来建立和验证人工神经网络（ANN）算法，以检测睡眠。我们还测试了ANN模型提高Biologix系统OSA诊断精度的假设。方法参与者和数据收集研究。

Data availability

数据可用性

The data that support the findings of this study are available from Biologix Sistemas S.A., but restrictions apply to the availability of these data, which were used under license for the current study and are not publicly available. Data are, however, available from the authors upon reasonable request and with the permission of Biologix Sistemas S.A..

支持本研究结果的数据可从Biologix Sistemas S.A.获得，但这些数据的可用性受到限制，这些数据是在当前研究的许可下使用的，并且不公开。但是，作者可根据合理要求并经Biologix Sistemas S.A.许可获得数据。。

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Nat. Sci. Sleep 1009–1021 (2020).Download referencesAcknowledgementsSleep laboratory of the Heart Institute (where PSG were performed), study participants and each coauthor are acknowledged.Author informationAuthors and AffiliationsBiologix Sistemas S.A., São Paulo, SP, BrazilDiego Munduruca Domingues, Paloma Rodrigues Rocha, Ana Cláudia M.

自然科学。睡眠1009-1021（2020）。。作者信息作者和附属机构Biologix Sistemas S.A.，圣保罗，SP，巴西Munduruca Domingues，Paloma Rodrigues Rocha，Ana Cláudia M。

V. Miachon & Filipe SoaresLaboratório do Sono, LIM 63, Divisão de Pneumologia, Instituto do Coração, InCor, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, Eneas de Carvalho Aguiar 44, 8º andar, São Paulo, SP, 05403-900, BrazilSara Quaglia de Campos Giampá, Pedro R. Genta & Geraldo Lorenzi-FilhoAuthorsDiego Munduruca DominguesView author publicationsYou can also search for this author in.

V.Miachon&Filipe Soares睡眠实验室，LIM 63，心脏研究所肺病科，InCor，Hospital das Clínicas HCFMUSP，圣保罗大学，Eneas de Carvalho Aguiar 44，8ºandar，圣保罗，SP，05403-900，巴西Sara Quaglia de Campos Giampá，Pedro R.Genta&Geraldo Lorenzi Filho作者Diego Munduruca Domingues查看作者出版物您也可以在中搜索这位作者。

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PubMed Google ScholarContributionsD.M.D., A.C.M.V.M., and F.S. led the development and validation of the artificial neural network algorithm. D.M.D and P.R.R. analyzed the results. D.M.D, P.R.R, A.C.M.V.M., S.Q.C.G and G.L.F wrote the initial manuscript draft with input from all authors.

PubMed谷歌学术贡献SD。M、 D.，A.C.M.V.M.和F.S.领导了人工神经网络算法的开发和验证。D、 M.D和P.R.R.分析了结果。D、 M.D，P.R.R，A.C.M.V.M.，S.Q.C.G和G.L.F在所有作者的意见下撰写了初稿。

P.R.G contributed substantially to the data analysis, interpretation of the data, or a combination thereof. All authors reviewed the manuscript.Corresponding authorCorrespondence to.

P、 R.G为数据分析，数据解释或其组合做出了重大贡献。所有作者都审阅了手稿。对应作者对应。

Diego Munduruca Domingues.Ethics declarations

迭戈·蒙杜鲁卡·多明格斯。道德宣言

Competing interests

相互竞争的利益

Authors Diego Munduruca Domingues, Paloma Rodrigues Rocha, Ana Cláudia M. V. Miachon and Filipe Soares are employees of Biologix. Geraldo Lorenzi-Filho is co-founder of Biologix. This research was funded by Biologix Sistemas S.A., Brazil.

作者Diego Munduruca Domingues、Paloma Rodrigues Rocha、Ana Cláudia M.V.Miachon和Filipe Soares是Biologix的员工。Geraldo Lorenzi Filho是Biologix的联合创始人。这项研究由巴西Biologix Sistemas S.A.资助。

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Reprints and permissionsAbout this articleCite this articleDomingues, D.M., Rocha, P.R., Miachon, A.C.M.V. et al. Sleep prediction using data from oximeter, accelerometer and snoring for portable monitor obstructive sleep apnea diagnosis.

转载和许可本文引用本文Domingues，D.M.，Rocha，P.R.，Miachon，A.C.M.V。等人。使用血氧仪，加速度计和打鼾数据进行睡眠预测，用于便携式监护仪阻塞性睡眠呼吸暂停的诊断。

Sci Rep 14, 24562 (2024). https://doi.org/10.1038/s41598-024-75935-8Download citationReceived: 19 February 2024Accepted: 09 October 2024Published: 19 October 2024DOI: https://doi.org/10.1038/s41598-024-75935-8Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

。https://doi.org/10.1038/s41598-024-75935-8Download引文收到日期：2024年2月19日接受日期：2024年10月9日发布日期：2024年10月19日OI：https://doi.org/10.1038/s41598-024-75935-8Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

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KeywordsArtificial neural networkSleep predictionObstructive sleep apnea

关键词人工神经网络睡眠预测阻塞性睡眠呼吸暂停