Ruhan Liu1,2, Chenyang Li1, Huajun Xu1, Kejia Wu1, Xinyi Li1, Yupu Liu1, Jie Yuan1, Lili Meng1, Jianyin Zou1, Weijun Huang1, Hongliang Yi1, Bin Sheng2, Jian Guan1, Shankai Yin1. 1. Department of Otolaryngology Head and Neck Surgery and Shanghai Key Laboratory of Sleep Disordered Breathing & Otolaryngological Institute of Shanghai Jiao Tong University, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, People's Republic of China. 2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Abstract
Purpose: Misdiagnosis and missed diagnosis of sleep-disordered breathing (SDB) is common because polysomnography (PSG) is time-consuming, expensive, and uncomfortable. The use of recording methods based on the oxygen saturation (SpO2) signals detected by wearable devices is impractical and inaccurate for extracting signal features and detecting apnoeic events. We propose a method to automatically detect the apnoea-based SpO2 signal segments and compute the apnoea-hypopnea index (AHI) for SDB screening and grading. Patients and Methods: First, apnoea-related desaturation segments in raw SpO2 signals were detected; global features were extracted from whole night signals. Then, the SpO2 signal segments and global features were fed into a bi-directional long short-term memory convolutional neural network model to identify apnoea-related and non-apnoea-related events. The apnoea-related segments were used to assess the AHI. Results: The model was trained on 500 individuals and tested on 8131 individuals from two public hospitals and one private centre. In the testing data, the classification accuracy for apnoea-related segments was 84.3%. Individuals with SDB (AHI 15) were identified with a mean accuracy of 88.95%. Conclusion: Using automatic SDB detection based on SpO2 signals can accurately screen for SDB.
Purpose: Misdiagnosis and missed diagnosis of sleep-disordered breathing (SDB) is common because polysomnography (PSG) is time-consuming, expensive, and uncomfortable. The use of recording methods based on the oxygen saturation (SpO2) signals detected by wearable devices is impractical and inaccurate for extracting signal features and detecting apnoeic events. We propose a method to automatically detect the apnoea-based SpO2 signal segments and compute the apnoea-hypopnea index (AHI) for SDB screening and grading. Patients and Methods: First, apnoea-related desaturation segments in raw SpO2 signals were detected; global features were extracted from whole night signals. Then, the SpO2 signal segments and global features were fed into a bi-directional long short-term memory convolutional neural network model to identify apnoea-related and non-apnoea-related events. The apnoea-related segments were used to assess the AHI. Results: The model was trained on 500 individuals and tested on 8131 individuals from two public hospitals and one private centre. In the testing data, the classification accuracy for apnoea-related segments was 84.3%. Individuals with SDB (AHI 15) were identified with a mean accuracy of 88.95%. Conclusion: Using automatic SDB detection based on SpO2 signals can accurately screen for SDB.
Authors: W Ward Flemons; Michael R Littner; James A Rowley; Peter Gay; W McDowell Anderson; David W Hudgel; R Douglas McEvoy; Daniel I Loube Journal: Chest Date: 2003-10 Impact factor: 9.410
Authors: Daniel Álvarez; María L Alonso-Álvarez; Gonzalo C Gutiérrez-Tobal; Andrea Crespo; Leila Kheirandish-Gozal; Roberto Hornero; David Gozal; Joaquín Terán-Santos; Félix Del Campo Journal: J Clin Sleep Med Date: 2017-05-15 Impact factor: 4.062