Automatic Sleep Stage Classification for the Obstructive Sleep Apnea Patients with Feature Mining

Abstract

Automatic sleep scoring systems have being much more attention in 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 in real life data. One can find many high accuracy studies in literature using standard database but when it comes to the using real data reaching such a high performances 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 case of 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 literature for a real-data application.