Segmentation of cardiac sound signals by removing murmurs using constrained tunable-Q wavelet transform

The automatic segmentation of cardiac sound signals into heart beat cycles is generally required for the diagnosis of heart valve disorders. In this paper, a new method for segmentation of the cardiac sound signals using tunable-Q wavelet transform (TQWT) has been presented. The murmurs from cardiac sound signals are removed by suitably constraining TQWT based decomposition and reconstruction. The Q-factor, redundancy parameter and number of stages of decomposition of the TQWT are adapted to the desired statistical properties of the murmur-free reconstructed cardiac sound signals. The envelope based on cardiac sound characteristic waveform (CSCW) is extracted after the removal of low energy components from the reconstructed cardiac sound signals. Then the heart beat cycles are derived from the original cardiac sound signals by mapping the required timing information of CSCW which is obtained using established methods. The experimental results are included in order to show the effectiveness of the proposed method for segmentation of cardiac sound signals in comparison with other existing methods for various clinical cases.