Accurate identification of oxygen desaturation status in COPD by using classifier ensemble

The accurate identification of oxygen desaturation (OD) status plays critical role in the clinic diagnosis of chronic obstructive pulmonary disease (COPD), which is a common disease related to the lungs and respiratory tract of the human body. This paper focuses on a specific type of OD status, i.e., exercise-induced oxygen desaturation (EIOD) status in COPD, and try to further improve the performance of EIOD status identification. We propose a new and effective EIOD status identification method by using classifier ensemble strategy. In the proposed method, five different features of each data point from the time series of SpO2 and pulse are extracted and then combined to form the discriminative feature of the corresponding data point; then, multiple base classifiers with different balanced training subsets are trained and then integrated by using AdaBoost Algorithm. The comparative computational results on the 6-min walk test (6MWT) of the recruited participants show that the proposed method achieved the best global performance with AUC (Area Under Curve) value of 0.8532, indicating that the proposed method can be effectively used for the identification of EIOD and could assist the clinic diagnosis of COPD.