Sleep prediction using data from oximeter, accelerometer and snoring for portable monitor obstructive sleep apnea diagnosis

• Published in: Scientific reports Vol. 14; no. 1; pp. 24562 - 9
• Main Authors: Domingues, Diego Munduruca, Rocha, Paloma Rodrigues, Miachon, Ana Cláudia M. V., Giampá, Sara Quaglia de Campos, Soares, Filipe, Genta, Pedro R., Lorenzi-Filho, Geraldo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.10.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 692/700/139; Accelerometers; Accelerometry - instrumentation; Accelerometry - methods; Adult; Aged; Algorithms; Apnea; Artificial neural network; Body mass index; Female; Humanities and Social Sciences; Humans; Male; Middle Aged; multidisciplinary; Neural networks; Neural Networks, Computer; Obstructive sleep apnea; Oximetry - instrumentation; Oximetry - methods; Polysomnography - instrumentation; Polysomnography - methods; Predictions; Science; Science (multidisciplinary); Sensitivity analysis; Sleep - physiology; Sleep apnea; Sleep Apnea, Obstructive - diagnosis; Sleep Apnea, Obstructive - physiopathology; Sleep disorders; Sleep prediction; Smartphone; Snoring - diagnosis; Snoring - physiopathology; Obstructive sleep apnea; Artificial neural network; Sleep prediction
• ISSN: 2045-2322; 2045-2322
• 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: 56 ± 11 years; body mass index: 30.9 ± 4.6 kg/m 2 , apnea-hypopnea index [AHI]: 35 ± 30 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.