Hiroshi Nakano1, Tomokazu Furukawa1, Takeshi Tanigawa2. 1. Sleep Disorders Centre, National Hospital Organization Fukuoka National Hospital, Yakatabaru, Minmi-ku, Fukuoka City, Japan. 2. Department of Public Health, Graduate School of Medicine, Juntendo University, Hongo, Bunkyo-ku, Tokyo, Japan.
Abstract
STUDY OBJECTIVES: Portable devices for home sleep apnea testing are often limited by their inability to discriminate sleep/wake status, possibly resulting in underestimations. Tracheal sound (TS), which can be visualized as a spectrogram, carries information about apnea/hypopnea and sleep/wake status. We hypothesized that image analysis of all-night TS recordings by a deep neural network (DNN) would be capable of detecting breathing events and classifying sleep/wake status. The aim of this study is to develop a DNN-based system for sleep apnea testing and validate it using a large sampling of polysomnography (PSG) data. METHODS: PSG examinations for the evaluation of sleep-disordered breathing (SDB) were performed for 1,852 patients: 1,548 PSG records were used to develop the system, and the remaining 304 records were used for validation. TS spectrogram images were obtained every 60 seconds and labeled with the PSG scoring results (breathing event and sleep/wake status), then introduced to DNN learning. Two different DNNs were trained for breathing status and sleep/wake status, respectively. RESULTS: A DNN with convolutional layers showed the best performance for discriminating breathing status. The same DNN structure was trained for sleep/wake discrimination. In the validation study, the DNN analysis was capable of discriminating the sleep/wake status with reasonable accuracy. The diagnostic sensitivity, specificity, and area under the receiver operating characteristic curves for diagnosis of SDB with apnea-hypopnea index of > 5, 15, and 30 were 0.98, 0.76, and 0.99; 0.97, 0.90, and 0.99; and 0.92, 0.94, and 0.98, respectively. CONCLUSIONS: The developed system using the TS DNN analysis has a good performance for SDB testing. CITATION: Nakano H, Furukawa T, Tanigawa T. Tracheal sound analysis using a deep neural network to detect sleep apnea. J Clin Sleep Med. 2019;15(8): 1125-1133.
STUDY OBJECTIVES: Portable devices for home sleep apnea testing are often limited by their inability to discriminate sleep/wake status, possibly resulting in underestimations. Tracheal sound (TS), which can be visualized as a spectrogram, carries information about apnea/hypopnea and sleep/wake status. We hypothesized that image analysis of all-night TS recordings by a deep neural network (DNN) would be capable of detecting breathing events and classifying sleep/wake status. The aim of this study is to develop a DNN-based system for sleep apnea testing and validate it using a large sampling of polysomnography (PSG) data. METHODS: PSG examinations for the evaluation of sleep-disordered breathing (SDB) were performed for 1,852 patients: 1,548 PSG records were used to develop the system, and the remaining 304 records were used for validation. TS spectrogram images were obtained every 60 seconds and labeled with the PSG scoring results (breathing event and sleep/wake status), then introduced to DNN learning. Two different DNNs were trained for breathing status and sleep/wake status, respectively. RESULTS: A DNN with convolutional layers showed the best performance for discriminating breathing status. The same DNN structure was trained for sleep/wake discrimination. In the validation study, the DNN analysis was capable of discriminating the sleep/wake status with reasonable accuracy. The diagnostic sensitivity, specificity, and area under the receiver operating characteristic curves for diagnosis of SDB with apnea-hypopnea index of > 5, 15, and 30 were 0.98, 0.76, and 0.99; 0.97, 0.90, and 0.99; and 0.92, 0.94, and 0.98, respectively. CONCLUSIONS: The developed system using the TS DNN analysis has a good performance for SDB testing. CITATION: Nakano H, Furukawa T, Tanigawa T. Tracheal sound analysis using a deep neural network to detect sleep apnea. J Clin Sleep Med. 2019;15(8): 1125-1133.
Authors: Miguel Marino; Yi Li; Michael N Rueschman; J W Winkelman; J M Ellenbogen; J M Solet; Hilary Dulin; Lisa F Berkman; Orfeu M Buxton Journal: Sleep Date: 2013-11-01 Impact factor: 5.849
Authors: Rahel A Teferra; Brydon J B Grant; Jesse W Mindel; Tauseef A Siddiqi; Imran H Iftikhar; Fatima Ajaz; Jose P Aliling; Meena S Khan; Stephen P Hoffmann; Ulysses J Magalang Journal: Ann Am Thorac Soc Date: 2014-09
Authors: Richard B Berry; Rohit Budhiraja; Daniel J Gottlieb; David Gozal; Conrad Iber; Vishesh K Kapur; Carole L Marcus; Reena Mehra; Sairam Parthasarathy; Stuart F Quan; Susan Redline; Kingman P Strohl; Sally L Davidson Ward; Michelle M Tangredi Journal: J Clin Sleep Med Date: 2012-10-15 Impact factor: 4.062
Authors: Indu Ayappa; Robert G Norman; David Whiting; Albert H W Tsai; Fiona Anderson; Emma Donnely; David J Silberstein; David M Rapoport Journal: Sleep Date: 2009-01 Impact factor: 5.849
Authors: Nathan Zavanelli; Hojoong Kim; Jongsu Kim; Robert Herbert; Musa Mahmood; Yun-Soung Kim; Shinjae Kwon; Nicholas B Bolus; F Brennan Torstrick; Christopher S D Lee; Woon-Hong Yeo Journal: Sci Adv Date: 2021-12-22 Impact factor: 14.136