A Home Sleep Apnea State Monitoring System using a Stacked Autoencoder

Ischemic heart disease is one of the most common causes of death in the world. Recent studies show that it may be caused by sleep apnea, or disordered breathing during sleep; however, sleep apnea lacks subjective symptoms. Therefore, an effective method is required to monitor sleep apnea states whil...

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Bibliographic Details
Published in:Proceedings of IEEE Sensors ... pp. 1 - 4
Main Authors: Takao, Ikuya, Nishio, Keita, Kaburagi, Takashi, Kumagai, Satoshi, Matsumoto, Toshiyuki, Kurihara, Yosuke
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2019
Subjects:
apnea state estimation
Autoencoders
Feedforward neural networks
Frequency estimation
Heart
Monitoring
pressure sensor
Pressure sensors
Sleep apnea
stacked autoencoder
State estimation
Time-frequency analysis
Vectors
ISSN:2168-9229
Online Access:Get full text
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Summary:Ischemic heart disease is one of the most common causes of death in the world. Recent studies show that it may be caused by sleep apnea, or disordered breathing during sleep; however, sleep apnea lacks subjective symptoms. Therefore, an effective method is required to monitor sleep apnea states while a subject is at home. In this context, this study proposes a method to estimate apnea conditions using an unconstrained sensing system placed under a mattress. The system provides the probability of the user being in an apnea state for given units of time. To reduce the effects from each individual user and their sleeping position, we applied a stacked autoencoder to obtain feature vectors. These are given to a feed-forward neural network with a two-dimensional output layer. The two-dimensional output is converted to probability values by a softmax function, and labels with a higher probability are estimated as an apnea state.
ISSN:2168-9229
DOI:10.1109/SENSORS43011.2019.8956822