Automatic Sleep Stage Classification for the Obstructive Sleep Apnea

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.

Automated summary

Automatic sleep scoring systems have gained more attention in recent decades. Despite various studies in this field, the accuracy of these methods is still below acceptable limits for real-life data. In this study, five different datasets were prepared using 124 individuals, including 93 unhealthy and 31 healthy individuals. Various classifiers were applied to these datasets using different feature sets and feature selection techniques to search for the highest classification accuracy. The Bagged Tree classifier achieved a relatively high accuracy of 95.06% with the use of 14 features out of a total of 136 features, surpassing previous literature for real-data applications.