Biologix Sistemas S.A., São Paulo, SP, Brazil.
Laboratório 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, Brazil.
Sci Rep. 2024 Oct 19;14(1):24562. doi: 10.1038/s41598-024-75935-8.
The 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. A total of 268 patients with suspected obstructive sleep apnea (OSA) were submitted to standard polysomnography (PSG) with simultaneous Biologix (age: years; body mass index: , apnea-hypopnea index [AHI]: events/h). Biologix channels were input features for construction an ANN model to predict sleep. 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. 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.
本研究旨在构建和验证一种人工神经网络（ANN）算法，使用带有内置加速度计的高分辨率血氧仪和带有打鼾记录及云端分析功能的智能手机应用程序（Biologix 系统）的数据来预测睡眠。共有 268 例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）检查，同时进行 Biologix 监测（年龄： 岁；体重指数： ，呼吸暂停低通气指数[AHI]： 次/小时）。Biologix 通道是用于构建 ANN 模型以预测睡眠的输入特征。采用 k 折交叉验证方法（k=10），确保所有睡眠研究（N=268；246,265 个 epoch）在所有迭代中都包含在训练和测试中。最终的 ANN 模型作为所有折叠的平均性能进行评估，其睡眠检测的灵敏度、特异性和准确性分别为 91.5%、71.0%和 86.1%。与没有睡眠预测的 Biologix 氧减指数（ODI）相比，PSG-AHI 与具有睡眠预测的 Biologix-ODI（Biologix-Sleep-ODI）之间的偏差（平均差值）显著降低（3.40 与 1.02 次/小时，p<0.001）。我们得出结论，使用血氧仪、加速度计和打鼾数据的 ANN 模型进行睡眠预测是准确的，可以提高 Biologix 系统 OSA 诊断的准确性。
