Sleep apnea detection using deep learning
Sleep apnea is the cessation of airflow at least 10 seconds and it is the type of breathing disorder in which breathing stops at the time of sleeping. The proposed model uses type 4 sleep study which focuses more on portability and the reduction of the signals. The main limitations of type 1 full ni...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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University North
2019-01-01
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| Series: | Tehnički Glasnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/333666 |
| _version_ | 1827282553924485120 |
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| author | Hnin Thiri Chaw Sinchai Kamolphiwong Krongthong Wongsritrang |
| author_facet | Hnin Thiri Chaw Sinchai Kamolphiwong Krongthong Wongsritrang |
| author_sort | Hnin Thiri Chaw |
| collection | DOAJ |
| description | Sleep apnea is the cessation of airflow at least 10 seconds and it is the type of breathing disorder in which breathing stops at the time of sleeping. The proposed model uses type 4 sleep study which focuses more on portability and the reduction of the signals. The main limitations of type 1 full night polysomnography are time consuming and it requires much space for sleep recording such as sleep lab comparing to type 4 sleep studies. The detection of sleep apnea using deep convolutional neural network model based on SPO2 sensor is the valid alternative for efficient polysomnography and it is portable and cost effective. The total number of samples from SPO2 sensors of 50 patients that is used in this study is 190,000. The performance of the overall accuracy of sleep apnea detection is 91.3085% with the loss rate of 2.3 using cross entropy cost function using deep convolutional neural network. |
| first_indexed | 2024-04-24T09:21:12Z |
| format | Article |
| id | doaj.art-3c2cdf544d964e4a9e3592c196d2952d |
| institution | Directory Open Access Journal |
| issn | 1846-6168 1848-5588 |
| language | English |
| last_indexed | 2024-04-24T09:21:12Z |
| publishDate | 2019-01-01 |
| publisher | University North |
| record_format | Article |
| series | Tehnički Glasnik |
| spelling | doaj.art-3c2cdf544d964e4a9e3592c196d2952d2024-04-15T15:53:51ZengUniversity NorthTehnički Glasnik1846-61681848-55882019-01-0113426126610.31803/tg-20191104191722Sleep apnea detection using deep learningHnin Thiri Chaw0Sinchai Kamolphiwong1Krongthong Wongsritrang2Dept of Computer Engineering, Prince of Songkla University, 90112 Hat Yai, ThailandDept of Computer Engineering, Prince of Songkla University, 90112 Hat Yai, ThailandDept. of Otolaryngology Head Neck Surgery, Songklanagrind Hospital, 90112 Hat Yai, ThailandSleep apnea is the cessation of airflow at least 10 seconds and it is the type of breathing disorder in which breathing stops at the time of sleeping. The proposed model uses type 4 sleep study which focuses more on portability and the reduction of the signals. The main limitations of type 1 full night polysomnography are time consuming and it requires much space for sleep recording such as sleep lab comparing to type 4 sleep studies. The detection of sleep apnea using deep convolutional neural network model based on SPO2 sensor is the valid alternative for efficient polysomnography and it is portable and cost effective. The total number of samples from SPO2 sensors of 50 patients that is used in this study is 190,000. The performance of the overall accuracy of sleep apnea detection is 91.3085% with the loss rate of 2.3 using cross entropy cost function using deep convolutional neural network.https://hrcak.srce.hr/file/333666continuous single bio-parameter recordingdeep convolutional neural networkdeep learningtype 4 sleep studyportable sleep apnea detection |
| spellingShingle | Hnin Thiri Chaw Sinchai Kamolphiwong Krongthong Wongsritrang Sleep apnea detection using deep learning Tehnički Glasnik continuous single bio-parameter recording deep convolutional neural network deep learning type 4 sleep study portable sleep apnea detection |
| title | Sleep apnea detection using deep learning |
| title_full | Sleep apnea detection using deep learning |
| title_fullStr | Sleep apnea detection using deep learning |
| title_full_unstemmed | Sleep apnea detection using deep learning |
| title_short | Sleep apnea detection using deep learning |
| title_sort | sleep apnea detection using deep learning |
| topic | continuous single bio-parameter recording deep convolutional neural network deep learning type 4 sleep study portable sleep apnea detection |
| url | https://hrcak.srce.hr/file/333666 |
| work_keys_str_mv | AT hninthirichaw sleepapneadetectionusingdeeplearning AT sinchaikamolphiwong sleepapneadetectionusingdeeplearning AT krongthongwongsritrang sleepapneadetectionusingdeeplearning |