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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.
Afiliación
  • Vaquerizo-Villar F; Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain. Electronic address: fernando.vaquerizo@gib.tel.uva.es.
  • Gutiérrez-Tobal GC; Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain.
  • Calvo E; Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
  • Álvarez D; Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain; Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, Valladolid, Spain.
  • Kheirandish-Gozal L; Departments of Neurology and Child Health and Child Health Research Institute, The University of Missouri School of Medicine, Columbia, MO, USA.
  • Del Campo F; Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain; Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, Valladolid, Spain.
  • Gozal D; Office of The Dean, Joan C. Edwards School of Medicine, Marshall University, 1600 Medical Center Dr, Huntington, WV, 25701, USA.
  • Hornero R; Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain.
Comput Biol Med ; 165: 107419, 2023 10.
Article en En | MEDLINE | ID: mdl-37703716
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Síndromes de la Apnea del Sueño / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Child / Humans Idioma: En Revista: Comput Biol Med Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Síndromes de la Apnea del Sueño / Aprendizaje Profundo Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Child / Humans Idioma: En Revista: Comput Biol Med Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos