In order to assist cardiac diagnosis by phonocardiography, the automated identification of fundamental heart sounds for heart beat segmentation in a cardiac cycle plays a significant role in signal processing. Recent advancements in signal processing have also shown the potential of multifractality in biomedical applications. Hence, in this paper, the multifractal property of heart sounds is utilized to identify first and second heart sounds. The root mean square (rms) fluctuation used to obtain multifractal/singularity spectrum is used to decompose the heart sound into its own fractally-important components in time domain along with simultaneous Gaussianity test to filter out fundamental components. The performance is evaluated on an experimental database of 23 different heart sounds and 6 patients' recordings done in a real clinical environment. Simulation results have shown that it is a promising approach in Heart Sound Segmentation (HSS).