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

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: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>56</mml:mn> <mml:mo>±</mml:mo> <mml:mn>11</mml:mn></mml:mrow> </mml:math> years; body mass index: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>30.9</mml:mn> <mml:mo>±</mml:mo> <mml:mn>4.6</mml:mn></mml:mrow> </mml:math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mtext>kg/m</mml:mtext> <mml:mn>2</mml:mn></mml:msup> </mml:math> , apnea-hypopnea index [AHI]: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>35</mml:mn> <mml:mo>±</mml:mo> <mml:mn>30</mml:mn></mml:mrow> </mml:math> 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.

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Publication Details

Journal
Scientific Reports
Published
2024-10-19
DOI
https://doi.org/10.1038/s41598-024-75935-8
Citations
3
Primary Topic
Obstructive Sleep Apnea Research
Type
article
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0.98
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Sleep prediction using data from oximeter, accelerometer and snoring for portable monitor obstructive sleep apnea diagnosis

Filipe Soares, Geraldo Lorenzi‐Filho, Sara Quaglia de Campos Giampá, Diego Munduruca Domingues et al.
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article

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

Filipe Soares, Geraldo Lorenzi‐Filho, Sara Quaglia de Campos Giampá, Diego Munduruca Domingues, Paulo Rocha, Pedro R. Genta, Ana Cláudia M. V. Miachon
article en
3 citations

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: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>56</mml:mn> <mml:mo>±</mml:mo> <mml:mn>11</mml:mn></mml:mrow> </mml:math> years; body mass index: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>30.9</mml:mn> <mml:mo>±</mml:mo> <mml:mn>4.6</mml:mn></mml:mrow> </mml:math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mtext>kg/m</mml:mtext> <mml:mn>2</mml:mn></mml:msup> </mml:math> , apnea-hypopnea index [AHI]: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>35</mml:mn> <mml:mo>±</mml:mo> <mml:mn>30</mml:mn></mml:mrow> </mml:math> 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.

Scientific ReportsVol. 14(1)
TiGenix (Spain) (ES), Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (BR)
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Obstructive Sleep Apnea Research
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