| Autor: |
Diego Munduruca Domingues, Paloma Rodrigues Rocha, Ana Cláudia M. V. Miachon, Sara Quaglia de Campos Giampá, Filipe Soares, Pedro R. Genta, Geraldo Lorenzi-Filho |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-024-75935-8 |
| Popis: |
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 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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