Matrix of Lags: A tool for analysis of multiple dependent time series applied for CAP scoring-Reference-Cited by-同舟云学术

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Matrix of Lags: A tool for analysis of multiple dependent time series applied for CAP scoring

Published:2020-06 Issue: Volume:189 Page:105314
ISSN:0169-2607
Container-title:Computer Methods and Programs in Biomedicine
language:en
Short-container-title:Computer Methods and Programs in Biomedicine

Author:

Mendonça Fábio,Mostafa Sheikh Shanawaz,Morgado-Dias Fernando,Ravelo-García Antonio G.

Funder

Portuguese Foundation for Science and Technology

ARDITI

Regional Development European Funds

Região Autónoma da Madeira

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications,Software

Reference54 articles.

1. Time Series Analysis and Its Applications;Shumway,2017

2. EEG arousals: scoring rules and examples. A preliminary report from the Sleep Disorders Atlas Task Force of the American Sleep Disorder Association;Bonnet;Sleep,1992

3. Predicting EEG complexity from sleep macro and microstructure;Chouvarda;Phys. Measur.,2011

4. Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep;Terzano;Sleep Med.,2001

5. Cyclic alternating pattern (CAP): the marker of sleep instability;Parrino;Sleep Med. Rev.,2012

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1. CAPSCNet: A novel scattering network for automated identification of phasic cyclic alternating patterns of human sleep using multivariate EEG signals;Computers in Biology and Medicine;2023-09

2. Towards automatic EEG cyclic alternating pattern analysis: a systematic review;Biomedical Engineering Letters;2023-07-19

3. Computerized detection of cyclic alternating patterns of sleep: A new paradigm, future scope and challenges;Computer Methods and Programs in Biomedicine;2023-06

4. Automated Explainable Detection of Cyclic Alternating Pattern (CAP) Phases and Sub-Phases Using Wavelet-Based Single-Channel EEG Signals;IEEE Access;2023

5. Automated classification of cyclic alternating pattern sleep phases in healthy and sleep-disordered subjects using convolutional neural network;Computers in Biology and Medicine;2022-07

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