Tracheal Sound Analysis Using a Deep Neural Network to Detect Sleep Apnea.

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

SUBMITTER: Nakano H 

PROVIDER: S-EPMC6707047 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature