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 pres...
Saved in:
Main Authors: | Hyojin Lee, You Rim Choi, Hyun Kyung Lee, Jaemin Jeong, Joopyo Hong, Hyun-Woo Shin, Hyung-Sin Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-01-01
|
Series: | npj Digital Medicine |
Online Access: | https://doi.org/10.1038/s41746-024-01378-0 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
MDCKE: Multimodal deep-context knowledge extractor that integrates contextual information
by: Hyojin Ko, et al.
Published: (2025-04-01) -
Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions
by: Jin-Hyun Park, et al.
Published: (2025-01-01) -
Advancing Model Explainability: Visual Concept Knowledge Distillation for Concept Bottleneck Models
by: Ju-Hwan Lee, et al.
Published: (2025-01-01) -
Automatic Recognition and Detection System Based on Machine Vision
by: Lu Wang, et al.
Published: (2022-01-01) -
Modification of Mechanical Properties of Ti–6Al–4V Using L-PBF for Anatomical Plates
by: Soumyabrata Basak, et al.
Published: (2025-01-01)