Hierarchical multi-scale dynamic graph analysis for early detection of change in EEG signals-Reference-Cited by-同舟云学术

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Hierarchical multi-scale dynamic graph analysis for early detection of change in EEG signals

Published:2024-12 Issue: Volume:98 Page:106734
ISSN:1746-8094
Container-title:Biomedical Signal Processing and Control
language:en
Short-container-title:Biomedical Signal Processing and Control

Author:

He Guangshuo,Lu Guoliang,Sun Mingxu^ORCID,Shang Wei

Publisher

Elsevier BV

Reference36 articles.

1. Principles of time–frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection;Boashash;Pattern Recognit.,2015

2. Inappropriate assumptions about EEG state changes and their impact on the quantification of EEG state dynamics;Koenig;Neuroimage,2016

3. Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis;Gajic;Front. Comput. Neurosci.,2015

4. Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study;Boashash;Knowl. Based Syst.,2016

5. FreSpeD: Frequency-specific change-point detection in epileptic seizure multi-channel EEG data;Schroder;J. Amer. Statist. Assoc.,2018

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