| Presentation | 2019-11-15 Sleep Apnea Detection Using Deep Convolutional Neural Network Hnin Thiri Chaw, Sinchai Kamolphiwong, Krongthong Wongsritrang, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | deep convolutonal neural network / sleep apnea detection / type 4 sleep studies / polysomnography / spo2 / oximetry / deep learning / sleep apnea prediction |
| Paper # | IA2019-39 |
| Date of Issue | 2019-11-07 (IA) |
| Conference Information | |
| Committee | IA |
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| Conference Date | 2019/11/14(2days) |
| 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 |
| Chair | Hiroyuki Osaki(Kwansei Gakuin Univ.) |
| Vice Chair | Rei Atarashi(IIJ) / Toru Kondo(Hiroshima Univ.) / Hiroshi Yamamoto(Ritsumeikan Univ.) |
| Secretary | Rei Atarashi(Kwansei Gakuin Univ.) / Toru Kondo(KDDI Research) / Hiroshi Yamamoto(NEC) |
| Assistant | Kenji Ohira(Osaka Univ.) / Daiki Nobayashi(Kyushu Inst. of Tech.) / Ryohei Banno(Tokyo Inst. of Tech.) |
| Paper Information | |
| Registration To | Technical Committee on Internet Architecture |
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| Language | ENG |
| 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) |
| Date | 2019-11-15 |
| Paper # | IA2019-39 |
| Volume (vol) | vol.119 |
| Number (no) | IA-291 |
| Page | pp.pp.67-71(IA), |
| #Pages | 5 |
| Date of Issue | 2019-11-07 (IA) |