1. Sleep prediction using data from oximeter, accelerometer and snoring for portable monitor obstructive sleep apnea diagnosis
- Author
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Diego Munduruca Domingues, Paloma Rodrigues Rocha, Ana Cláudia M. V. Miachon, Sara Quaglia de Campos Giampá, Filipe Soares, Pedro R. Genta, and Geraldo Lorenzi-Filho
- Subjects
Artificial neural network ,Sleep prediction ,Obstructive sleep apnea ,Medicine ,Science - Abstract
Abstract 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: $$56\pm 11$$ 56 ± 11 years; body mass index: $$30.9\pm 4.6$$ 30.9 ± 4.6 $$\hbox {kg/m}^{2}$$ kg/m 2 , apnea-hypopnea index [AHI]: $$35\pm 30$$ 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
- Published
- 2024
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