An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea

Vaquerizo-Villar, Fernando; Gutiérrez-Tobal, Gonzalo C.; Calvo, Eva; Álvarez, Daniel; Kheirandish-Gozal, Leila; del Campo, Félix; Gozal, David; Hornero, Roberto

doi:10.1016/j.compbiomed.2023.107419

Cited by 7 publications

(2 citation statements)

References 51 publications

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“…In this study, we only focused on traditional feature-based methods for disease classification without exploring the potential of deep learning methods for processing high-dimensional features like images and sounds. With the advantages of deep learning in sleep research ( 54 ), introducing deep learning methods into studying OSA disease classification may have potential benefits. Future work can explore deep learning models such as convolution neural networks (CNN) to improve the modeling ability of complex feature relationships.…”

Section: Discussionmentioning

confidence: 99%

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis

Qin,

Zhang,

et al. 2024

Front. Pediatr.

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ObjectiveThe objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments.Patients and methodsThis study was conducted at Beijing Children's Hospital from April 2018 to October 2019. The participants in this study were 2464 children aged 3–18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG). Participants’ data were randomly divided into a training set and a testing set at a ratio of 8:2. The elastic net algorithm was used for feature selection to simplify the model. Stratified 10-fold cross-validation was repeated five times to ensure the robustness of the results.ResultsFeature selection using Elastic Net resulted in 47 features for AHI ≥5 and 31 features for AHI ≥10 being retained. The machine learning model using these selected features achieved an average AUC of 0.73 for AHI ≥5 and 0.78 for AHI ≥10 when tested externally, outperforming models based on PSG questionnaire features. Linear Discriminant Analysis using the selected features identified OSA with a sensitivity of 44% and specificity of 90%, providing a feasible clinical alternative to PSG for stratifying OSA severity.ConclusionsThis study shows that a machine learning model based on children's clinical features effectively identifies OSA in children. Establishing a machine learning screening model based on the clinical features of the target population may be a feasible clinical alternative to nocturnal OSA sleep diagnosis.

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Section: Discussionmentioning

confidence: 99%

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis

Qin,

Zhang,

et al. 2024

Front. Pediatr.

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“…Finally, artificial intelligence and XAI are gaining importance in the evolution of sleep research, as depicted in two of the published studies. Favored by the above-mentioned increasing in data collection, which is mandatorily required for training successful models, the combination of deep learning and XAI is now providing very accurate methods for the purpose they are designed, while also uncovering new sleep-related knowledge based on the explanations of the decisions automatically made by these models (Vaquerizo-Villar et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

Editorial: Unraveling sleep and its disorders using novel analytical approaches, volume II

Gutiérrez-Tobal,

Kheirandish-Gozal,

Gozal

et al. 2023

Front. Neurosci.

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Visual identification of sleep spindles in EEG waveform images using deep learning object detection (YOLOv4 vs YOLOX)

Fraiwan,

Khasawneh

2024

Cluster Comput

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An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea

Cited by 7 publications

References 51 publications

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis

Editorial: Unraveling sleep and its disorders using novel analytical approaches, volume II

Visual identification of sleep spindles in EEG waveform images using deep learning object detection (YOLOv4 vs YOLOX)

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