基于DHMM的低心率变异性心音的分割方法

针对现有心音定位分割方法精度有限的难题，提出了一种对心率变异性较低的信号建模分割方法。首先，通过集合经验模态分解（Ensemble empirical mode decomposition，EEMD）使用有效的本征模态函数（Intrinsic mode function，IMF）分量来表征心音信号，提高心音信号的可分析性；然后，通过基础心音与非基础心音间的高斯约束关系建立高斯混合模型（Gaussian mixture model，GMM）；接着，优化隐马尔可夫模型（Hidden Markov model, HMM）并建立基于时间相关性的隐马尔可夫模型（Duration-dependent hidden Markov model，DHMM），更简洁地描述分割模型，降低算法复杂度；最后，通过时域特征区分出s₁，收缩期，s₂和舒张期。将本文算法与经典Hilbert算法和逻辑回归的隐半马尔科夫模型（Logistic regression hidden semi-Markov model，LRHSMM）算法进行了对比，实验结果表明，本文算法的检出正确率和运算耗时等评价指标更优。

Low Heart Rate Variability Heart Sound Segmentation Method Using DHMM

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.