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Abstract: The precursory signals, which are preceding the impulsive P-waves, are analyzed in this study. These signals cause an error in automatic P-wave detectors which motivated us to propose an algorithm to identify these signals automatically. The first detail of multi-resolution analysis (MRA) of discrete wavelet transform (DWT) is used to detect these precursory signals. The proposed algorithm is built based on the parameters of the second order autoregressive (AR) model. 34 impulsive P-wave segments with precursory signals are used to test the ability of the proposed algorithm to detect these signals. Length of each precursory signal is calculated manually and using the proposed algorithm, where the difference between the two calculations is the error of the automatic detection. The proposed algorithm used both Haar and Daubechies wavelet filters of order 2 (abbreviated as Dau(2)) for comparison. It is found that the Dau(2) wavelet filter gave better results than Haar wavelet filter as will be discussed later. The mean and standard deviation of the error, when using Dau(2) wavelet, are −1 and 4 samples, respectively. [Copyright &y& Elsevier]