An expert system for automated classification of phases in cyclic alternating patterns of sleep using optimal wavelet‐based entropy features

Author:

Sharma Manish1ORCID,Bhurane Ankit A.2ORCID,Acharya U. Rajendra345

Affiliation:

1. Department of Electrical and Computer Science Engineering Institute of Infrastructure, Technology, Research and Management (IITRAM) Ahmedabad Gujarat India

2. Department of Electronics and Communication Engineering Visvesvaraya National Institute of Technology Nagpur Maharashtra India

3. Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore

4. Department of Bioinformatics and Medical Engineering Asia University Wufeng Taiwan

5. International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University Kumamoto Japan

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Reference54 articles.

1. Selection of mother wavelet functions for multi‐channel eeg signal analysis during a working memory task;Al‐Qazzaz N. K.;Sensors,2015

2. Aasm scoring manual updates for 2017 (version 2.4);Journal of Clinical Sleep Medicine,2017

3. The aasm manual for the scoring of sleep and associated events;Berry R. B.;Rules, Terminology and Technical Specifications, Darien, Illinois, American Academy of Sleep Medicine,2012

4. Interrater reliability between scorer from eight european sleep labs in subjects with different sleep disorders;Danker‐Hopfe H.;Somnologie,2001

5. Automated phase classification in cyclic alternating patterns in sleep stages using wigner–ville distribution based features;Dhok S.;Computers in Biology and Medicine,2020

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