Detection and classification of heart murmurs play an important role in accurate diagnosis of different types of heart dysfunctions. In this paper, we present a noise-robust method for detection and classification of heart murmurs using stationary wavelet transform (SWT) and Hilbert phase envelope. The proposed method consists of five major stages: SWT based PCG signal decomposition for identifying heart sound (HS) including S1, S2, S3 and S4, and heart murmur(HM) subbands, Hilbert phase envelope based boundary determination, temporal feature extraction, murmur detection and classification rule. The boundaries of local acoustic HS segments are determined using the positive slope of instantaneous phase waveform of the smooth absolute envelope.
The temporal features such as amplitude, duration, zerocrossing rate, interval, onset and offset time-instants of the detected HS and HM segments are used at the classification stage. The performance of the proposed method is tested and validated using a wide variety of normal and pathological signals containing different patterns of heart sounds and murmurs. The method achieves a probability of correctly detecting HM segments Pms=100%, a probability of correctly detecting HS segments Phs=97.33% and probability of falsely detecting segments Pfs=1.33% for SNR value of 15 dB, and murmur classification accuracy ranging from 82.76% to 100%.