Özet
Sleep apnea is described as a cessation of breath for at least 10 seconds during the
sleeping. These apneas can occur in hundreds depending on the severity of the disease
during the sleep. Busy schedules and high costs of sleep laboratories in hospitals make
apnea diagnosis in the society difficult task. In order decrease loads of sleep laboratories
and increase the number diagnostic attempts with a low cost, there is a need for using
portable apnea devices which can trace possible apnea patients out of hospitals.
The purpose of this study is to analyze and reveal the proper combination sets of
physiological signals for detecting apnea episodes in order to decide whether the
standard but more complicated polysomnography test stage might be required or not for
possible apnea patients by examining physiological signals recorded by portable
recording devices used for prescreening purposes. Thus, air flow, oxygen saturation and
ECG signals are used separately for apnea detections. For this reason, neural networks
are trained and tested by extracted dynamic features associated with each signal. For a
sample case, the neural network trained with hybrid (including three channels) data
iv
generated from the recordings obtained by the portable recording device has shown
89.6% high detection rate based on the expert scores.
In the last phase of the study, a software interface was also developed in order to examine
obtained physiological signals for possible apnea patients and the data analysis useful
for apnea scoring and labeling. With the tools provided by the interface using third level
portable apnea device definition, three channels, namely air flow, oxygen saturation and
ECG signal, can be examined comprehensively for general apnea analysis. In order to
have detailed analysis, embedded neural network topologies can be chosen through the
designed interface and associated features that are derived for each corresponding
channels can be used in this platform for implementing training and testing phases.
Künye
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