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

Authors: HÜSEYİN GÜRÜLER, MESUT ŞAHİN, ABDULLAH FERİKOĞLU

Abstract: Many articles that appeared in the literature agreed upon the feasibility of diagnosing obstructive sleep apnea (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 (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

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