A Hierarchical Approach for the Diagnosis of Sleep Disorders Using Convolutional Recurrent Neural Network-Reference-Cited by-同舟云学术

A Hierarchical Approach for the Diagnosis of Sleep Disorders Using Convolutional Recurrent Neural Network

Published:2023 Issue: Volume:11 Page:125244-125255
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Wadichar Aditya¹^ORCID,Murarka Shruti¹^ORCID,Shah Dhruvi¹^ORCID,Bhurane Ankit¹^ORCID,Sharma Manish²^ORCID,Mir Hasan S.³,Acharya U. Rajendra⁴^ORCID

Affiliation:

1. Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, India

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

3. Department of Electrical Engineering, American University of Sharjah, Sharjah, United Arab Emirates

4. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi Rd, Singapore

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10310136.pdf?arnumber=10310136

Reference56 articles.

1. An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank

2. Automatic Sleep Stage Classification Using Temporal Convolutional Neural Network and New Data Augmentation Technique from Raw Single-Channel EEG

3. Machine Learning and Integrative Analysis of Biomedical Big Data

4. A Deep Neural Model CNN-LSTM Network for Automated Sleep Staging Based on a Single-Channel EEG Signal

5. Automated Detection of Sleep Stages Using Energy-Localized Orthogonal Wavelet Filter Banks

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