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Paper Abstract and Keywords
Presentation 2019-11-15 11:25
Sleep Apnea Detection Using Deep Convolutional Neural Network
Hnin Thiri Chaw, Sinchai Kamolphiwong, Krongthong Wongsritrang (PSU) IA2019-39
Abstract (in Japanese) (See Japanese page) 
(in English) Sleep apnea is the breathing disorder in which the cessation of the air flow occurs while sleeping. Deep convolutional neural network is good at effective and powerful feature learning ability in order to detect sleep apnea. Moreover, it is also suitable for large and complex nature of the datasets such as the nature of polysomnography signals, EEG(electroencephalography) signal, ECG(electrocardiography) signal, oximetry signal. The nature of the respiratory signals is also very complex and it contain nasal pressure signal, thermistor signal, oximetry signal, thoracic signal and abdominal signal. In this paper, the detection of the sleep apnea using deep convolutional network will be used and the signal that is used in this paper is oximetry signal which is also known as SPO2 signal. In addition, the model that is used in this study is known as type 4 sleep studies which focus more on portability and reduction of the signals in order to cover the limitations of the type 1 sleep studies which require a number of signals known as polysomnography. The total number of SPO2 samples from 50 patients is 190,000 samples and subject specific scenario validation is used with a split rate of 0.2 in each empirical study. The performance of the overall accuracy of sleep apnea detection is 91.3085% with the loss rate the prediction of 2.3 using cross entropy cost function.
Keyword (in Japanese) (See Japanese page) 
(in English) deep convolutonal neural network / sleep apnea detection / type 4 sleep studies / polysomnography / spo2 / oximetry / deep learning / sleep apnea prediction  
Reference Info. IEICE Tech. Rep., vol. 119, no. 291, IA2019-39, pp. 67-71, Nov. 2019.
Paper # IA2019-39 
Date of Issue 2019-11-07 (IA) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee IA  
Conference Date 2019-11-14 - 2019-11-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Kwansei Gakuin University, Tokyo Marunouchi Campus (Sapia Tower) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) IA2019 - Workshop on Internet Architecture and Applications 2019 
Paper Information
Registration To IA 
Conference Code 2019-11-IA 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Sleep Apnea Detection Using Deep Convolutional Neural Network 
Sub Title (in English)  
Keyword(1) deep convolutonal neural network  
Keyword(2) sleep apnea detection  
Keyword(3) type 4 sleep studies  
Keyword(4) polysomnography  
Keyword(5) spo2  
Keyword(6) oximetry  
Keyword(7) deep learning  
Keyword(8) sleep apnea prediction  
1st Author's Name Hnin Thiri Chaw  
1st Author's Affiliation Prince of Songkla University (PSU)
2nd Author's Name Sinchai Kamolphiwong  
2nd Author's Affiliation Prince of Songkla University (PSU)
3rd Author's Name Krongthong Wongsritrang  
3rd Author's Affiliation Prince of Songkla University (PSU)
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Speaker
Date Time 2019-11-15 11:25:00 
Presentation Time 25 
Registration for IA 
Paper # IEICE-IA2019-39 
Volume (vol) IEICE-119 
Number (no) no.291 
Page pp.67-71 
#Pages IEICE-5 
Date of Issue IEICE-IA-2019-11-07 


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