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We propose a projection method based on singular value decomposition (SVD) to validate arterial blood pressure (ABP) signal in order to avoid artifacts and noise in subsequent processing. The projection has been done on 567 validated ABP beats collected from 51 patients hospitalized in University of California, Los Angeles Medical Center. Then, we compare the performance of the proposed projection method with that of a previously developed algorithm, signal abnormality index (SAI), which is a value- and trend-based approach, and has shown to be effective in cleaning the ABP waveforms. The testing dataset consists of 1336 ten-second ABP segments (18 472 ABP beats) of both valid and invalid pulses selected randomly from multiparameter intelligent monitoring for intensive care II database. The proposed projection approach that validates the signal based on the shape of the waveform achieves a true positive rate (TPR) of 99.06%, 5.43% higher than that of the SAI, and a false positive rate (FPR) of 7.69%, 17.38% lower than that of SAI. Integration of some of the SAI-value-based abnormality conditions to the validation process of SVD-based method can further improve the performance by reducing the FPR to 3.92%, while keeping the TPR at the high rate of 99.05%.