基于持续时间隐马尔可夫模型的心音分割算法

Segmentation of heart sound signals based on duration hidden Markov model

心音分割指对所获取的心音信号按心动周期对收缩期、舒张期等进行分隔,是进行心音分类前的关键步骤。针对不依赖心电图对心音信号直接分割准确度有限的难题,提出了一种基于持续时间隐马尔可夫模型的心音分割算法。首先对心音样本进行位置标注;然后采用自相关估计法对心音的心动周期持续时间进行估计,通过高斯混合分布对样本的状态持续时间进行建模;接着通过训练集信号对隐马尔可夫模型进行优化并建立基于持续时间的隐马尔可夫模型(DHMM);最后使用维特比算法对心音状态进行回溯得出S1、收缩期、S2、舒张期。使用500例心音样本对本文算法性能进行测试,平均评估精度分数(F1)为0.933,平均灵敏度为0.930,平均精确率为0.936。同其他算法相比,本文算法各项性能指标均有明显提升,证实了该算法具有较高的鲁棒性和抗噪声性能,为临床环境下所采集心音信号的特征提取与分析提供了一种新方法。

Heart sound segmentation is a key step before heart sound classification.It refers to the processing of the acquired heart sound signal that separates the cardiac cycle into systolic and diastolic,etc.To solve the accuracy limitation of heart sound segmentation without relying on electrocardiogram,an algorithm based on the duration hidden Markov model(DHMM)was proposed.Firstly,the heart sound samples were positionally labeled.Then autocorrelation estimation method was used to estimate cardiac cycle duration,and Gaussian mixture distribution was used to model the duration of sample-state.Next,the hidden Markov model(HMM)was optimized in the training set and the DHMM was established.Finally,the Viterbi algorithm was used to track back the state of heart sounds to obtain S1,systole,S2 and diastole.500 heart sound samples were used to test the performance of our algorithm.The average evaluation accuracy score(F1)was 0.933,the average sensitivity was 0.930,and the average accuracy rate was 0.936.Compared with other algorithms,the performance of our algorithm was more superior.It is proved that the algorithm has high robustness and anti-noise performance,which might provide a novel method for the feature extraction and analysis of heart sound signals collected in clinical environments.