Performance of a Convolutional Neural Network Derived from PPG Signal in Classifying Sleep Stages-Reference-Cited by-同舟云学术

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Performance of a Convolutional Neural Network Derived from PPG Signal in Classifying Sleep Stages

Published:2023 Issue: Volume: Page:1-15
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

Habib Ahsan¹^ORCID,Motin Mohammod Abdul²^ORCID,Penzel Thomas³,Palaniswami Marimuthu⁴,Yearwood John¹,Karmakar Chandan¹^ORCID

Affiliation:

1. School of Information Technology, Geelong, Australia

2. Department of Electrical and Electronic Engineering, Rajshahi University of Engineering and Technology, Bangladesh

3. Charite-Universitatsmedizin Berlin, Interdisciplinary Sleep Medicine Center, Berlin, Germany

4. Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/10/4359967/09940577.pdf?arnumber=9940577

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A machine-learning sleep-wake classification model using a reduced number of features derived from photoplethysmography and activity signals;Anais do XXIV Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2024);2024-06-25

2. From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability;Sensors;2023-11-09

3. Multi-stage sleep classification using photoplethysmographic sensor;Royal Society Open Science;2023-04

4. Recent Progress in Long-Term Sleep Monitoring Technology;Biosensors;2023-03-17

5. The Virtual Sleep Lab—A Novel Method for Accurate Four-Class Sleep Staging Using Heart-Rate Variability from Low-Cost Wearables;Sensors;2023-02-21

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