RT info:eu-repo/semantics/article
T1 An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea
A1 Vaquerizo Villar, Fernando
A1 Gutiérrez Tobal, Gonzalo César
A1 Calvo, Eva
A1 Álvarez, Daniel
A1 Kheirandish Gozal, Leila
A1 Campo Matias, Félix del
A1 Hornero Sánchez, Roberto
K1 Sueño, trastornos del
K1 Pediatría
K1 Deep learning
K1 Electroencephalogram (EEG)
K1 Pediatric obstructive sleep apnea (OSA)
K1 Aprendizaje profundo
K1 Electroencefalograma (EEG)
K1 Apnea obstructiva del sueño pediátrica (AOS)
K1 3201.10 Pediatría
AB Automatic deep-learning models used for sleep scoring in children with obstructive sleep apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings. Accordingly, we aimed to develop an accurate and interpretable deep-learning model for sleep staging in children using single-channel electroencephalogram (EEG) recordings. We used EEG signals from the Childhood Adenotonsillectomy Trial (CHAT) dataset (n = 1637) and a clinical sleep database (n = 980). Three distinct deep-learning architectures were explored to automatically classify sleep stages from a single-channel EEG data. Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable artificial intelligence (XAI) algorithm, was then applied to provide an interpretation of the singular EEG patterns contributing to each predicted sleep stage. Among the tested architectures, a standard convolutional neural network (CNN) demonstrated the highest performance for automated sleep stage detection in the CHAT test set (accuracy = 86.9% and five-class kappa = 0.827). Furthermore, the CNN-based estimation of total sleep time exhibited strong agreement in the clinical dataset (intra-class correlation coefficient = 0.772). Our XAI approach using Grad-CAM effectively highlighted the EEG features associated with each sleep stage, emphasizing their influence on the CNN's decision-making process in both datasets. Grad-CAM heatmaps also allowed to identify and analyze epochs within a recording with a highly likelihood to be misclassified, revealing mixed features from different sleep stages within these epochs. Finally, Grad-CAM heatmaps unveiled novel features contributing to sleep scoring using a single EEG channel. Consequently, integrating an explainable CNN-based deep-learning model in the clinical environment could enable automatic sleep staging in pediatric sleep apnea tests.
PB Elsevier
SN 0010-4825
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/61507
UL https://uvadoc.uva.es/handle/10324/61507
LA eng
NO Computers in Biology and Medicine, 2023, vol. 165, 107419
NO Producción Científica
DS UVaDOC
RD 21-may-2024