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

Hiroshi Nakano | Zendy; Tomokazu Furukawa | Zendy; Takeshi Tanigawa | Zendy

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

Author(s) -

Hiroshi Nakano,

Tomokazu Furukawa,

Takeshi Tanigawa

Publication year - 2019

Publication title -

journal of clinical sleep medicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.529

H-Index - 92

eISSN - 1550-9397

pISSN - 1550-9389

DOI - 10.5664/jcsm.7804

Subject(s) - polysomnography , medicine , apnea , sleep apnea , hypopnea , breathing , spectrogram , sleep (system call) , audiology , obstructive sleep apnea , artificial intelligence , anesthesia , computer science , operating system

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.

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