Abstract: Heart rate variability（HRV） signals contain a large amount of physiological and pathological information of the heart and cardiovascular system, and in-depth analysis of them can help diagnose and warn of heart diseases such as arrhythmia. The paper uses the MIT-BIH ECG database to extract two types of ECG data of normal heart rhythm and arrhythmia, and performs signal preprocessing to eliminate noise interference; the wavelet transform is used to extract the modulus extreme value and zero-crossing point of wavelet coefficients to obtain the ECG R wave Signal.The HRV sequence is obtained by the first-order difference of ECG R-wave signal.The three nonlinear characteristics of wavelet entropy, approximate entropy, and basic scale entropy of the HRV signal are extracted, and the difference between normal ECG and arrhythmia ECG characteristics is statistically tested. The simulation results show that the nonlinear characteristics of the HRV signal can identify the normal heart rhythm and arrhythmia ECG signals effectively.