U-Sleep’s resilience to AASM guidelines
Abstract AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications, e.g., recommended EEG derivations, to detailed...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-03-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-023-00784-0 |
| _version_ | 1797556205927792640 |
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| author | Luigi Fiorillo Giuliana Monachino Julia van der Meer Marco Pesce Jan D. Warncke Markus H. Schmidt Claudio L. A. Bassetti Athina Tzovara Paolo Favaro Francesca D. Faraci |
| author_facet | Luigi Fiorillo Giuliana Monachino Julia van der Meer Marco Pesce Jan D. Warncke Markus H. Schmidt Claudio L. A. Bassetti Athina Tzovara Paolo Favaro Francesca D. Faraci |
| author_sort | Luigi Fiorillo |
| collection | DOAJ |
| description | Abstract AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications, e.g., recommended EEG derivations, to detailed sleep scoring rules accordingly to age. Automated sleep scoring systems have always largely exploited the standards as fundamental guidelines. In this context, deep learning has demonstrated better performance compared to classical machine learning. Our present work shows that a deep learning-based sleep scoring algorithm may not need to fully exploit the clinical knowledge or to strictly adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a state-of-the-art sleep scoring algorithm, can be strong enough to solve the scoring task even using clinically non-recommended or non-conventional derivations, and with no need to exploit information about the chronological age of the subjects. We finally strengthen a well-known finding that using data from multiple data centers always results in a better performing model compared with training on a single cohort. Indeed, we show that this latter statement is still valid even by increasing the size and the heterogeneity of the single data cohort. In all our experiments we used 28528 polysomnography studies from 13 different clinical studies. |
| first_indexed | 2024-03-10T16:59:31Z |
| format | Article |
| id | doaj.art-01eb57002a1e48c58a4a4acaa23e06d7 |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-10T16:59:31Z |
| publishDate | 2023-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-01eb57002a1e48c58a4a4acaa23e06d72023-11-20T11:01:00ZengNature Portfolionpj Digital Medicine2398-63522023-03-01611910.1038/s41746-023-00784-0U-Sleep’s resilience to AASM guidelinesLuigi Fiorillo0Giuliana Monachino1Julia van der Meer2Marco Pesce3Jan D. Warncke4Markus H. Schmidt5Claudio L. A. Bassetti6Athina Tzovara7Paolo Favaro8Francesca D. Faraci9Institute of Informatics, University of BernInstitute of Informatics, University of BernSleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of BernSleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of BernSleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of BernSleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of BernSleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of BernInstitute of Informatics, University of BernInstitute of Informatics, University of BernInstitute of Digital Technologies for Personalized Healthcare ∣ MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern SwitzerlandAbstract AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications, e.g., recommended EEG derivations, to detailed sleep scoring rules accordingly to age. Automated sleep scoring systems have always largely exploited the standards as fundamental guidelines. In this context, deep learning has demonstrated better performance compared to classical machine learning. Our present work shows that a deep learning-based sleep scoring algorithm may not need to fully exploit the clinical knowledge or to strictly adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a state-of-the-art sleep scoring algorithm, can be strong enough to solve the scoring task even using clinically non-recommended or non-conventional derivations, and with no need to exploit information about the chronological age of the subjects. We finally strengthen a well-known finding that using data from multiple data centers always results in a better performing model compared with training on a single cohort. Indeed, we show that this latter statement is still valid even by increasing the size and the heterogeneity of the single data cohort. In all our experiments we used 28528 polysomnography studies from 13 different clinical studies.https://doi.org/10.1038/s41746-023-00784-0 |
| spellingShingle | Luigi Fiorillo Giuliana Monachino Julia van der Meer Marco Pesce Jan D. Warncke Markus H. Schmidt Claudio L. A. Bassetti Athina Tzovara Paolo Favaro Francesca D. Faraci U-Sleep’s resilience to AASM guidelines npj Digital Medicine |
| title | U-Sleep’s resilience to AASM guidelines |
| title_full | U-Sleep’s resilience to AASM guidelines |
| title_fullStr | U-Sleep’s resilience to AASM guidelines |
| title_full_unstemmed | U-Sleep’s resilience to AASM guidelines |
| title_short | U-Sleep’s resilience to AASM guidelines |
| title_sort | u sleep s resilience to aasm guidelines |
| url | https://doi.org/10.1038/s41746-023-00784-0 |
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