Feature selection on single-lead ECG for obstructive sleep apnea diagnosis

Authors : Hüseyin GÜRÜLER, Mesut ŞAHİN, Abdullah FERİKOĞLU

Pages : 465-478

Doi:10.3906/elk-1207-132

View : 13 | Download : 4

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Many articles that appeared in the literature agreed upon the feasibility of diagnosing obstructive sleep apnea insert ignore into journalissuearticles values(OSA); with a single-lead electrocardiogram. Although high accuracies have been achieved in detection of apneic episodes and classification into apnea/hypopnea, there has not been a consensus on the best method of selecting the feature parameters. This study presents a classification scheme for OSA using common features belonging to the time domain, frequency domain, and nonlinear calculations of heart rate variability analysis, and then proposes a method of feature selection based on correlation matrices insert ignore into journalissuearticles values(CMs);. The results show that the CMs can be utilized in minimizing the feature sets used for any type of diagnosis.
Keywords : Heart rate variability, sleep apnea, feature selection, correlation matrices, diagnosing, classification