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

<h4>Study objectives</h4>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...

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Autores principales:	Diego Alvarez-Estevez, Roselyne M Rijsman
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/926826dd69a1409490f3273def0eae30
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spelling	oai:doaj.org-article:926826dd69a1409490f3273def0eae302021-12-02T20:18:01ZInter-database validation of a deep learning approach for automatic sleep scoring.1932-620310.1371/journal.pone.0256111https://doaj.org/article/926826dd69a1409490f3273def0eae302021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256111https://doaj.org/toc/1932-6203<h4>Study objectives</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.Diego Alvarez-EstevezRoselyne M RijsmanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0256111 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Diego Alvarez-Estevez Roselyne M Rijsman Inter-database validation of a deep learning approach for automatic sleep scoring.
description	<h4>Study objectives</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.
format	article
author	Diego Alvarez-Estevez Roselyne M Rijsman
author_facet	Diego Alvarez-Estevez Roselyne M Rijsman
author_sort	Diego Alvarez-Estevez
title	Inter-database validation of a deep learning approach for automatic sleep scoring.
title_short	Inter-database validation of a deep learning approach for automatic sleep scoring.
title_full	Inter-database validation of a deep learning approach for automatic sleep scoring.
title_fullStr	Inter-database validation of a deep learning approach for automatic sleep scoring.
title_full_unstemmed	Inter-database validation of a deep learning approach for automatic sleep scoring.
title_sort	inter-database validation of a deep learning approach for automatic sleep scoring.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/926826dd69a1409490f3273def0eae30
work_keys_str_mv	AT diegoalvarezestevez interdatabasevalidationofadeeplearningapproachforautomaticsleepscoring AT roselynemrijsman interdatabasevalidationofadeeplearningapproachforautomaticsleepscoring
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Inter-database validation of a deep learning approach for automatic sleep scoring.

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