Abstract
An automatic sleep apnea detection algorithm is essential not only for alleviating the onus of physicians of analyzing a high volume of data but also for making a portable sleep quality evaluation device feasible. Most prior studies are either multi-lead based or yield poor accuracy which hinder the aforementioned goals. In this work, we propound a statistical and spectral feature based method for automated apnea detection from singlelead electrocardiogram. The efficacy of the selected features is demonstrated by intuitive, graphical and statistical validation. RUSBoost is introduced for sleep apnea classification. Again, most of the existing works focus on the feature extraction part. The effect of various classification models is poorly studied. Besides propounding an automated sleep apnea screening method, we study the performance of eight well-know classifiers for our feature extraction scheme. The optimal choices of parameters for RUSBoost are also inspected. The results of our experiments manifest that the proposed method outperforms the state-of-theart ones in terms of accuracy.