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

作者：Hyojin Lee, You Rim Choi, Hyun Kyung Lee, Jaemin Jeong, Joopyo Hong, Hyun‐Woo Shin, Hyung‐Sin Kim · 发表于：npj Digital Medicine · 年份：2025 · DOI：10.1038/s41746-024-01378-0 · 被引用次数：21 · 研究领域：EEG and Brain-Computer Interfaces、Obstructive Sleep Apnea Research、Sleep and Wakefulness Research

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, alongside high-resolution heatmaps highlighting essential features and relevance scores for adjacent epochs' influence on sleep stage predictions. Together, these explanations reinforce the scoring consistency of SleepXViT, making it both reliable and interpretable, thereby facilitating the synergy between the AI model and human scorers in clinical settings.