2023
DOI: 10.1007/s41237-023-00199-x
|View full text |Cite
|
Sign up to set email alerts
|

Generalisability of sleep stage classification based on interbeat intervals: validating three machine learning approaches on self-recorded test data

Abstract: Classifying sleep stages is an important basis for neuroscience, health sciences, psychology and many other fields. However, the manual determination of sleep stages is tedious and time consuming. Therefore, the development of automatic sleep stage classifiers based on data collected with low-cost sensor systems is an important research area. This study aims to analyse the generalisability of different machine learning approaches for sleep stage classification. We train three different models (random forest, C… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
references
References 29 publications
0
0
0
Order By: Relevance