Bochun Wang1,2,3, Xuanyu Yi4, Jiandong Gao4,5, Yanru Li1,2,3, Wen Xu1,2,3, Ji Wu4,5, Demin Han1,2,3. 1. Beijing Tongren Hospital, Capital Medical University, Beijing, China. 2. Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, China. 3. Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, China. 4. Department of Electronic Engineering, Tsinghua University, Beijing, China. 5. Center for Big Data and Clinical Research, Institute for Precision Medicine, Tsinghua University, Beijing, China.
Abstract
STUDY OBJECTIVES: The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. METHODS: Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. RESULTS: A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. CONCLUSIONS: Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. CITATION: Wang B, Yi X, Gao J, et al. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.
STUDY OBJECTIVES: The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. METHODS: Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. RESULTS: A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. CONCLUSIONS: Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. CITATION: Wang B, Yi X, Gao J, et al. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.
Authors: Kun Qian; Christoph Janott; Vedhas Pandit; Zixing Zhang; Clemens Heiser; Winfried Hohenhorst; Michael Herzog; Werner Hemmert; Bjorn Schuller Journal: IEEE Trans Biomed Eng Date: 2016-10-21 Impact factor: 4.538
Authors: Simon A Joosten; Kais Hamza; Scott Sands; Anthony Turton; Philip Berger; Garun Hamilton Journal: Respirology Date: 2012-01 Impact factor: 6.424