首页|Time-frequency ridge characterisation of sleep stage transitions: Towards improving electroencephalogram annotations using an advanced visualisation technique

Time-frequency ridge characterisation of sleep stage transitions: Towards improving electroencephalogram annotations using an advanced visualisation technique

扫码查看
原文链接
  • NETL
  • NSTL
  • Elsevier
Manual sleep stage scoring of polysomnography recordings is an expensive and time-consuming process, further complicated by inconsistent sleep stage agreement among sleep experts (clinicians and sleep technologists). Hence, development of automated sleep scoring algorithms are an emerging topic of interest. Automation typically mimics the clinical decision path by implementing a series of predefined rules, such as the American Academy of Sleep Medicine's (AASM) scoring manual. Recently, data driven methods have emerged using machine or deep learning. Both manual and automated methods of scoring have known limitations; primarily, unacceptable variation in agreement between different scorers and algorithms. Within the literature, electroencephalogram (EEG) frequency is an important feature considered by both sleep experts and automated approaches for classifying sleep stages. This study presents a novel approach to sleep stage analysis, by developing a methodology to precisely determine the temporal location of sleep stage transitions. The current gold standard fails to identify such transitional changes, which leads to poor inter-scorer reliability. Therefore, development and implementation of such methodologies is a crucial, but overlooked, step in improving the consistency of scoring within sleep studies. In this work, EEG time-frequency ridge analysis was used to characterise the dominant frequency component of EEG signals in time, at the point of sleep stage transition. An in-depth analysis of N3 → N2 and N2 → N3 transitions in the 2018 PhysioNet challenge "You Snooze, You Win" and the Wisconsin Sleep Cohort (WSC) datasets (n = 994, n = 742; approximately 13,888 h of sleep data) showed consistent time-frequency patterns at the point of transition, from one sleep stage to another. This methodology allows simple and 'interpretable' features to be generated in future work, to precisely identify the temporal location of sleep stage transitions with the aim of improving inter-scorer reliability.

Automatic scoringElectroencephalographySleep stagingTime-frequency analysisInterpretable AI

Christopher McCausland、Pardis Biglarbeigi、Raymond Bond、Golnaz Yadollahikhales、Alan Kennedy、Anna Sigridur Islind、Erna Sif Arnardottir、Dewar Finlay

展开 >

Nanotechnology and Integrated BioEngineering Centre (NIBEC), Ulster University, Belfast, BT15 1ED, UK

School of Science and Engineering, University of Dundee, Dundee, DD1 4HN, UK

School of Computing, Ulster University, Belfast, BT15 1ED, UK

Jona Goldrich Center for Alzheimers and Memory Disorders, Neurology Department, Cedars-Sinai Medical Centre, Los Angeles, CA 90048, USA

Department of Computer Science, Reykjavik University, Reykjavik, Iceland||Reykjavik University, Sleep Institute, Reykjavik, Iceland

展开 >

2025

10.1016/j.eswa.2024.125490

Expert systems with applications

Expert systems with applications

SCI
ISSN：0957-4174
年,卷(期)：2025.262(Mar.)
  • 61