Multi-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoring.

Fiorillo, Luigi; Pedroncelli, Davide; Agostini, Valentina; Favaro, Paolo; Faraci, Francesca Dalia (2023). Multi-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoring. Sleep, 46(5) Oxford University Press 10.1093/sleep/zsad028

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STUDY OBJECTIVES

Inter-scorer variability in scoring polysomnograms is a well-known problem. Most of the existing automated sleep scoring systems are trained using labels annotated by a single scorer, whose subjective evaluation is transferred to the model. When annotations from two or more scorers are available, the scoring models are usually trained on the scorer consensus. The averaged scorer's subjectivity is transferred into the model, losing information about the internal variability among different scorers. In this study, we aim to insert the multiple-knowledge of the different physicians into the training procedure. The goal is to optimize a model training, exploiting the full information that can be extracted from the consensus of a group of scorers.

METHODS

We train two lightweight deep learning based models on three different multi-scored databases. We exploit the label smoothing technique together with a soft-consensus (LSSC) distribution to insert the multiple-knowledge in the training procedure of the model. We introduce the averaged cosine similarity metric (ACS) to quantify the similarity between the hypnodensity-graph generated by the models with-LSSC and the hypnodensity-graph generated by the scorer consensus.

RESULTS

The performance of the models improves on all the databases when we train the models with our LSSC. We found an increase in ACS (up to 6.4%) between the hypnodensity-graph generated by the models trained with-LSSC and the hypnodensity-graph generated by the consensus.

CONCLUSION

Our approach definitely enables a model to better adapt to the consensus of the group of scorers. Future work will focus on further investigations on different scoring architectures and hopefully large-scale-heterogeneous multi-scored datasets.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF) > Computer Vision Group (CVG)
08 Faculty of Science > Institute of Computer Science (INF)

UniBE Contributor:

Favaro, Paolo

Subjects:

000 Computer science, knowledge & systems
600 Technology > 620 Engineering
500 Science > 510 Mathematics

ISSN:

1550-9109

Publisher:

Oxford University Press

Language:

English

Submitter:

Pubmed Import

Date Deposited:

13 Feb 2023 12:08

Last Modified:

11 May 2023 00:13

Publisher DOI:

10.1093/sleep/zsad028

PubMed ID:

36762998

Uncontrolled Keywords:

automatic sleep stage classification deep learning machine learning multi-scored sleep databases

BORIS DOI:

10.48350/178621

URI:

https://boris.unibe.ch/id/eprint/178621

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