A comparative study of various classifiers for automated sleep apnea screening based on single-lead electrocardiogram

Computerized sleep apnea detection is necessary to alleviate the onus of physicians of analyzing a high volume of data. The overall performance of an automated apnea detection scheme greatly depends of the choice of classifier. Most of the existing works focus on the feature extraction part. The effect of various classification models is poorly studied. In the present work, we employ statistical moment based features and Empirical Mode Decomposition to devise a feature extraction scheme. Furthermore, we study the performance of nine well-know classifiers for this feature extraction scheme- naive bayes, kNN, neural network, AdaBoost, Bagging, random forest, extreme learning machine (ELM), discriminant analysis and restricted boltzmann machine. The optimal choice of parameters of each of the classifiers is also studied. This study suggests that ELM is a promising classification model for automated sleep apnea detection.