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Spectrogram classification of patient chin electromyography based on deep learning: A novel method for accurate diagnosis obstructive sleep apnea

  • Published:2023-01 Issue: Volume:79 Page:104215
  • ISSN:1746-8094
  • Container-title:Biomedical Signal Processing and Control
  • language:en
  • Short-container-title:Biomedical Signal Processing and Control

Author:

Moradhasel Behrouz,Sheikhani Ali,Aloosh Oldooz,Jafarnia Dabanloo Nader

Publisher

Elsevier BV

Subject

Health Informatics,Signal Processing,Biomedical Engineering

Reference43 articles.

1. A Review of Obstructive Sleep Apnea Detection Approaches;Mendonca;IEEE J. Biomed. Health Inform,2019

2. R. Jayaraj, J. Mohan, A. Kanagasabai. A Review on Detection and Treatment Methods of Sleep Apnea, J. Clin. Diagn. Res. 11(2017) VE01–VE03.

3. A prospective study of sleep duration and coronary heart disease in women;Ayas;Arch. Intern. Med.,2003

4. J. Schneerson, Hand book of sleep medicine. Black Well science, Pages: 237, ISBN: 0-632-05135-3, 2010.

5. Obstructive sleep apnea syndrome;Massimo;Eur. J. Internal Med.,2012

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2. Sleep Apnea Detection Using Wireless Wearable Single-Lead ECG: A One Dimensional Convolutional Neural Network Approach;Digital Health Research;2024

3. Utilization of Chin EMG Variation During OSA Onset to Improve Apnea Classification;2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology;2023-12-07

4. Insomnia Detection using Machine Learning Classifiers;2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS);2023-10-27

5. OBSTRÜKTİF UYKU APNESİ TESPİTİNDE POLİSOMNOGRAFİYE ALTERNATİF YENİ YÖNTEMLER;Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi;2023-03-15
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