Explainable vision transformer for automatic visual sleep staging on multimodal PSG signals.

ABSTRACT: Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the 'black-box' nature. In this study, we present SleepXViT, an automatic sleep staging system using Vision Transformer (ViT) that provides intuitive, consistent explanations by mimicking human 'visual scoring'. Tested on KISS-a PSG image dataset from 7745 patients across four hospitals-SleepXViT achieved a Macro F1 score of 81.94%, outperforming baseline models and showing robust performances on public datasets SHHS1 and SHHS2. Furthermore, SleepXViT offers well-calibrated confidence scores, enabling expert review for low-confidence predictions, al

SUBMITTER: Lee H 

PROVIDER: S-EPMC11762271 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature