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

ABSTRACT:

Introduction

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.

Methods

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.

Results

The pro

SUBMITTER: Zhang D 

PROVIDER: S-EPMC10326279 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature