Inter-database validation of a deep learning approach for automatic sleep scoring

• Autor: Alvarez-Estevez, Diego ; Rijsman, Roselyne M
• Qiu, Yuchen
• Assuntos: Algorithms ; Analysis ; Approximation ; Architecture ; Artificial neural networks ; Biology and Life Sciences ; Cable television broadcasting industry ; Computer and Information Sciences ; Computer architecture ; Databases, Factual - standards ; Datasets ; Deep learning ; Deep Learning - standards ; Electroencephalography - methods ; Engineering and Technology ; Humans ; Machine learning ; Medical informatics ; Medical research ; Medicine and Health Sciences ; Medicine, Experimental ; Methods ; Neural networks ; Neural Networks, Computer ; Neurophysiology ; Patients' rights ; Polysomnography - methods ; Predictions ; Privacy, Right of ; Research and Analysis Methods ; Sleep ; Sleep - physiology ; Sleep apnea ; Sleep disorders ; Sleep Stages - physiology ; Social Sciences ; Stages
• É parte de: PloS one, 2021-08, Vol.16 (8), p.e0256111-e0256111
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  Competing Interests: The authors have declared that no competing interests exist.
  
• Descrição: Development of inter-database generalizable sleep staging algorithms represents a challenge due to increased data variability across different datasets. Sharing data between different centers is also a problem due to potential restrictions due to patient privacy protection. In this work, we describe a new deep learning approach for automatic sleep staging, and address its generalization capabilities on a wide range of public sleep staging databases. We also examine the suitability of a novel approach that uses an ensemble of individual local models and evaluate its impact on the resulting inter-database generalization performance. A general deep learning network architecture for automatic sleep staging is presented. Different preprocessing and architectural variant options are tested. The resulting prediction capabilities are evaluated and compared on a heterogeneous collection of six public sleep staging datasets. Validation is carried out in the context of independent local and external dataset generalization scenarios. Best results were achieved using the CNN_LSTM_5 neural network variant. Average prediction capabilities on independent local testing sets achieved 0.80 kappa score. When individual local models predict data from external datasets, average kappa score decreases to 0.54. Using the proposed ensemble-based approach, average kappa performance on the external dataset prediction scenario increases to 0.62. To our knowledge this is the largest study by the number of datasets so far on validating the generalization capabilities of an automatic sleep staging algorithm using external databases. Validation results show good general performance of our method, as compared with the expected levels of human agreement, as well as to state-of-the-art automatic sleep staging methods. The proposed ensemble-based approach enables flexible and scalable design, allowing dynamic integration of local models into the final ensemble, preserving data locality, and increasing generalization capabilities of the resulting system at the same time.
  
• Editor: United States: Public Library of Science
  
• Idioma: Inglês