A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry

Jiménez-García, Jorge; García, María M.; Gutiérrez‐Tobal, Gonzalo C.; Kheirandish‐Gozal, Leila; Vaquerizo-Villar, Fernando; Álvarez, Daniel; Campo, Félix del; Gozal, David; Hornero, Roberto

doi:10.1016/j.compbiomed.2022.105784

Cited by 19 publications

(7 citation statements)

References 43 publications

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“…For instance, for the diagnosis of obstructive apnea, Alarcon et al combined SpO 2 , heart rate, thoracic respiratory effort, and abdominal-respiratory effort; nevertheless, Jimenez-Garcia et al combined SpO 2 and airflow signal (54,55). Since the test is closer to a full PSG by increasing the received signals, it is evident that the use of combined signals could increase the sensitivity and specificity of diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Systematic Review of Detecting Sleep Apnea Using Artificial Intelligence: An Insight to Convolutional Neural Network Method

Samadi,

Samadi

et al. 2024

Arch Neurosci

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Background: Sleep apnea is a prevalent sleep disorder, especially in males and older ages. The common diagnostic methods, including polysomnography (PSG), are expensive, difficult to perform, and time-consuming. Numerous studies are focusing on developing easy-to-perform methods based on artificial intelligence (AI) for the early diagnosis of sleep apnea. This systematic review aimed to gather current methods based on the convolutional neural network (CNN) for the diagnosis of sleep apnea. Methods: Three international electronic databases (PubMed, Web of Science [WoS], and Scopus) were searched from 2010 to October 2023. All studies that have developed CNN-based methods for the diagnosis of sleep apnea and have accomplished the performance tests were included. Finally, the characteristics of the studies were extracted and summarized. Results: A total of 36 studies were included in this systematic review. Various physiological signals have been proposed to detect sleep apnea, including electrocardiogram (ECG), blood oxygen saturation (SpO2), sound signals, respiratory signals, electroencephalogram (EEG), and nasal airflow. Electrocardiogram was the most frequently used signal in the studies, followed by SpO2. The highest reported accuracy was achieved by SpO2 or ECG-based methods and with a one-dimensional CNN (1D-CNN) classifier. Using multiple signals did not necessarily increase the performance of test results. Conclusions: Diagnostic methods based on CNN can be used only as screening tools or home diagnosis of sleep apnea. These methods are easy to perform and can only reduce the diagnostic costs and waiting time for a sleep study in special scenarios. Nevertheless, PSG is still the gold standard for the diagnosis of sleep disorders.

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Section: Discussionmentioning

confidence: 99%

Systematic Review of Detecting Sleep Apnea Using Artificial Intelligence: An Insight to Convolutional Neural Network Method

Samadi,

Samadi

et al. 2024

Arch Neurosci

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“…One such approach was proposed by Wu et al, 21 who developed a multilayer perceptron model based on single‐channel oxygen saturation and clinical features such as age, sex, BMI, adenoid, and tonsil size to detect pediatric OSA 21 . Another approach was proposed by Jiménez‐García et al, 22 who developed a 2D convolutional neural network to detect OSA in children using airflow and oximetry. Both studies reported high diagnostic accuracy in detecting SDB in children.…”

Section: Diagnosis Of Sdb: Beyond the Ahimentioning

confidence: 99%

Pediatric pulmonology year in review 2022: Sleep medicine

Cabrera,

Tapia

2023

Pediatric Pulmonology

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Pediatric pulmonology publishes original research, review articles, and case reports on a wide variety of pediatric respiratory disorders. In this article, we summarized the past year's publications in sleep medicine and reviewed selected literature from other journals in this field. We focused on original research articles exploring aspects of sleep‐disordered breathing in patients with underlying conditions such as asthma, neuromuscular disorders, and Down syndrome. We also explored sleep‐disordered breathing risk factors, monitoring, diagnosis, and treatment; and included recent recommendations for drug‐induced sleep endoscopy and ways to monitor and improve PAP adherence remotely.

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“…The proposed model is a custom CNN architecture, which was designed to tailor to the specific problem we address in our study. While the layer type and order of this architecture were inherited from previous works carried out in our research group to estimate the severity of pediatric OSA using SpO 2 and/or AF signals [39,42], the remaining CNN components were customized. As shown in Figure 2, our approach consists of three modules.…”

Section: Convolutional Neural Network Architecturementioning

confidence: 99%

“…Non-linear activation: It allows the network to learn complex and non-linear relationships from the feature maps [30,31]. We used the Rectified Linear Unit (ReLU) function for this purpose, which assigns zero to all negative values and keeps positive values unchanged [39,42].…”

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confidence: 99%

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

Barroso-García,

Fernández-Poyatos,

Sahelices

et al. 2023

Diagnostics

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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.

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A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry

Cited by 19 publications

References 43 publications

Systematic Review of Detecting Sleep Apnea Using Artificial Intelligence: An Insight to Convolutional Neural Network Method

Systematic Review of Detecting Sleep Apnea Using Artificial Intelligence: An Insight to Convolutional Neural Network Method

Pediatric pulmonology year in review 2022: Sleep medicine

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

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