Automatic Sleep Stage Classification for the Obstructive Sleep Apnea
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
Trans Tech Publications Ltd
Access Rights
info:eu-repo/semantics/closedAccess
Abstract
Automatic sleep scoring systems have been much more attention in the last decades. Whereas a wide variety of studies have been used in this subject area, the accuracies are still under acceptable limits to apply these methods to real-life data. One can find many high-accuracy studies in literature using a standard database but when it comes to using real data reaching such high performance is not straightforward. In this study, five distinct datasets were prepared using 124 persons including 93 unhealthy and 31 healthy persons. These datasets consist of time-, nonlinear-, welch-, discrete wavelet transform- and Hilbert-Huang transform features. By applying k-NN, Decision Trees, ANN, SVM, and Bagged Tree classifiers to these feature sets in various manners by using feature-selection highest classification accuracy was searched. The maximum classification accuracy was detected in the case of the Bagged Tree classifier as 95.06% with the use of 14 features among a total of 136 features. This accuracy is relatively high compared with the literature for a real-data application.
Description
Keywords
Signal Detection, Discrete Wavelet Transform, Hilbert-Huang Transform
Journal or Series
Journal Of Biomimetics Biomaterials And Biomedical Engineering
WoS Q Value
Scopus Q Value
Q4
Volume
60
