HRV is a nonstationary signal that includes sympathovagal balance (SB) information related to LF/HF ratio between the sympathetic and parasympathetic nervous systems. In this paper, a solution based on Daubechies wavelet transform (dbN) and multilayer perceptron neural network (MLPNN) has been presented for the determination of SB. HRV database obtained MIT-BIH arrhythmia database consisting of pairs of RR interval time sefies, recorded by implanted cardioverter defibrillators in 78 subjects. RMS values of approximation and detail components (Arms and Drms) obtained from dbN wavelet transform of HRV signals have been used as training data for MLPNN. Trains were realized in 5 different dbN with only Arms components, only Drms components and both of them and results were compared. Train accuracy and test accuracy results have been reached very successful percentage values that might be valuable for clinical applications. (C) 2008 Elsevier Inc. All fights reserved.