Aiming at the problem that the existing heart sound localization segmentation method has limited precision, a method of modeling and segmentation of heart sound signals with low heart rate variability is proposed. Firstly, the effective intrinsic mode function (IMF) component of the ensemble empirical mode decomposition (EEMD) is used to characterize the heart sound signal to improve the analyzability of heart sound signals. Then, the Gaussian mixture model(GMM) is established by the Gaussian constraint relationship between the basic heart sound and the non-basic heart sound. Next, the hidden Markov model (HMM) is optimized and the duration-dependent hidden Markov model (DHMM) is established, which can describe the segmtaention model more concisely and reduce the algorithm's complexity. Finally, the s₁, systolic phases, s₂, and diastolic phases are distinguished by time domain features. The proposed algorithm is compared with the classical Hilbert method and logistic regression hidden semi-Markov model(LRHSMM). Experimental results show that the proposed algorithm has better evaluation indicators such as detection accuracy and calculation time.