A two-branch trade-off neural network for balanced scoring sleep stages on multiple cohorts

Automatic sleep staging is a classification process with severe class imbalance and suffers from instability of scoring stage N1. Decreased accuracy in classifying stage N1 significantly impacts the staging of individuals with sleep disorders. We aim to achieve automatic sleep staging with expert-le...

Full description

Saved in:
Bibliographic Details
Published in:Frontiers in neuroscience 2023-06, Vol.17, p.1176551-1176551
Main Authors: Zhang, Di, Sun, Jinbo, She, Yichong, Cui, Yapeng, Zeng, Xiao, Lu, Liming, Tang, Chunzhi, Xu, Nenggui, Chen, Badong, Qin, Wei
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Alzheimer's disease
Automation
Datasets
Deep learning
Human performance
Mental disorders
multiple cohorts
N1 sleep
Neural networks
Neurological diseases
Neuroscience
polysomnography
Research methodology
Sleep
Sleep disorders
sleep staging
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Automatic sleep staging is a classification process with severe class imbalance and suffers from instability of scoring stage N1. Decreased accuracy in classifying stage N1 significantly impacts the staging of individuals with sleep disorders. We aim to achieve automatic sleep staging with expert-level performance in both N1 stage and overall scoring. A neural network model combines an attention-based convolutional neural network and a classifier with two branches is developed. A transitive training strategy is employed to balance universal feature learning and contextual referencing. Parameter optimization and benchmark comparisons are conducted using a large-scale dataset, followed by evaluation on seven datasets in five cohorts. The proposed model achieves an accuracy of 88.16%, Cohen's kappa of 0.836, and MF1 score of 0.818 on the SHHS1 test set, also with comparable performance to human scorers in scoring stage N1. Incorporating multiple cohort data improves its performance. Notably, the model maintains high performance when applied to unseen datasets and patients with neurological or psychiatric disorders. The proposed algorithm demonstrates strong performance and generalizablility, and its direct transferability is noteworthy among similar studies on automated sleep staging. It is publicly available, which is conducive to expanding access to sleep-related analysis, especially those associated with neurological or psychiatric disorders.
ISSN:1662-4548
1662-453X
1662-453X
DOI:10.3389/fnins.2023.1176551