Using Bootstrap AdaBoost with KNN for ECG-based automated obstructive sleep apnea detection

This paper presents an integrated Bootstrap AdaBoost with k-nearest neighbor (KNN) algorithm for obstructive sleep apnea (OSA) screening based on electrocardiogram (ECG) recordings during sleep. The proposed method processes single-lead ECG recordings for predicting the presence of major sleep apnea and provides a minute-by-minute analysis of disordered breathing. In our analysis, 35 recordings collected from the Physionet Apnea-ECG database were used as the training/testing dataset. A variety of features based on RR interval, an ECG-derived respiratory signal, and cardiopulmonary coupling techniques were employed. A Bootstrap AdaBoost with k-dimensional tree KNN was used as the classifier, adopting feature selection to optimize classifier performance. The Bootstrap AdaBoost with KDKNN (BA-KDKNN) algorithm reached an accuracy of 91.95%, sensitivity of 99.36%, and specificity of up to 89.02% with ten features.