结合SVM和香农能量的HSMM心音分割算法

doi:10.16337/j.1004-9037.2021.05.010

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结合SVM和香农能量的HSMM心音分割算法
DOI:
                        10.16337/j.1004-9037.2021.05.010
                    
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作者单位:江西理工大学信息工程学院，赣州 341000
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基金项目:国家自然科学基金(11864016)资助项目；江西省研究生创新专项基金(YC2019-S317)资助项目。

Heart Sound Segmentation Algorithm of HSMM Based on SVM and Shannon Energy

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School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000,China

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摘要:

针对基于逻辑回归的隐半马尔可夫模型中希尔伯特（Hilbert）变换提取的心音包络具有较大毛刺，提出一种结合支持向量机（Support vector machine， SVM）和香农能量的隐半马尔可夫模型（Hidden semi-Markov model， HSMM）心音分割算法。首先采用小波降噪的方法对心音进行降噪，接着根据R峰和T波标记心音，提取香农能量包络等特征，然后对结合逻辑回归模型（Logistic regression， LR）的HSMM相关参数进行训练，并借助Viterbi算法推测出最可能的状态。最后，通过SVM模型识别第一心音S₁和第二心音S₂。该算法无需设置硬阈值，有效地抑制了噪声，更有助于包络的提取。实验结果表明，提出的算法分割精确度较参考算法得到显著的提升，具有良好的抗噪性能，取得了更好的分割效果。

Abstract:

Aiming at the heart sound envelope burr produced by Hilbert transform in the hidden semi-Markov model（HSMM）based on logistic regression， an HSMM combining support vector machine （SVM） and Shannon energy is proposed. First， the wavelet denoising method is used to denoise the heart sound， the heart sound is labeled according to the R peak and T wave， and the Shannon energy envelope and other characteristics are extracted. Then， the HSMM related parameters are trained based on the logistic regression model （LR）， and the most possible state is deduced with the help of Viterbi algorithm. Finally， the first heart sound S₁ and the second heart sound S₂ are identified through the SVM model. The algorithm does not need to set a hard threshold， effectively suppresses noise， and is more helpful for envelope extraction. Experimental results show that the segmentation accuracy of the proposed algorithm is significantly improved compared with the reference algorithm， with good anti-noise performance and better segmentation results.