Physiological signals provide important clues in the diagnosis and prediction of disease. Analyzing these signals is important inhealth and medicine. In particular, data preprocessing for physiological signal analysis is a vital issue because missing values, noise,and outliers may degrade the analysis performance. In this paper, we propose PhysioCover, a system that can recover missing valuesof physiological signals that were monitored in real time. PhysioCover integrates a gradual method and EM-based PrincipleComponent Analysis (PCA). This approach can (1) more readily recover long- and short-term missing data than existing methods,such as traditional EM-based PCA, linear interpolation, 5-average and Missing Value Singular Value Decomposition (MSVD), (2)more effectively detect hidden variables than PCA and Independent component analysis (ICA), and (3) offer fast computation timethrough real-time processing. Experimental results with the physiological data of an intensive care unit show that the proposedmethod assigns more accurate missing values than previous methods.