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. 2023 Oct 12;13(20):3187.
doi: 10.3390/diagnostics13203187.

Prediction of the Sleep Apnea Severity Using 2D-Convolutional Neural Networks and Respiratory Effort Signals

Verónica Barroso-García  1   2 , Marta Fernández-Poyatos  1 , Benjamín Sahelices  3 , Daniel Álvarez  1   2 , David Gozal  4 , Roberto Hornero  1   2 , Gonzalo C Gutiérrez-Tobal  1   2
Affiliations

Prediction of the Sleep Apnea Severity Using 2D-Convolutional Neural Networks and Respiratory Effort Signals

Verónica Barroso-García et al. Diagnostics (Basel). .

Abstract

The high prevalence of sleep apnea and the limitations of polysomnography have prompted the investigation of strategies aimed at automated diagnosis using a restricted number of physiological measures. This study aimed to demonstrate that thoracic (THO) and abdominal (ABD) movement signals are useful for accurately estimating the severity of sleep apnea, even if central respiratory events are present. Thus, we developed 2D-convolutional neural networks (CNNs) jointly using THO and ABD to automatically estimate sleep apnea severity and evaluate the central event contribution. Our proposal achieved an intraclass correlation coefficient (ICC) = 0.75 and a root mean square error (RMSE) = 10.33 events/h when estimating the apnea-hypopnea index, and ICC = 0.83 and RMSE = 0.95 events/h when estimating the central apnea index. The CNN obtained accuracies of 94.98%, 79.82%, and 81.60% for 5, 15, and 30 events/h when evaluating the complete apnea hypopnea index. The model improved when the nature of the events was central: 98.72% and 99.74% accuracy for 5 and 15 events/h. Hence, the information extracted from these signals using CNNs could be a powerful tool to diagnose sleep apnea, especially in subjects with a high density of central apnea events.

Keywords: abdominal respiratory signal; central sleep apnea; convolutional neural network; deep learning; obstructive sleep apnea; thoracic respiratory signal.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Data distribution in training, validation, and test sets.
Figure 2
Figure 2
Architecture of the convolutional neural network (CNN) used in this study.
Figure 3
Figure 3
Kappa values obtained in the validation set during the CNN hyperparameter optimization process.
Figure 4
Figure 4
Scatter and Bland–Altman plots obtained by comparing the PSG-derived AHI and the CNN-estimated AHI in the test set considering (a,c) all apneic events (Global AHI) and (b,d) the central apneic events (Central AHI).
Figure 5
Figure 5
Confusion matrices obtained by the CNN models for the severity prediction from the global and central AHI in the test set, being 0: no apnea; 1: mild apnea; 2: moderate apnea; 3: severe apnea (no subjects with ≥30 central e/h).
See this image and copyright information in PMC

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