Discriminating Pregnancy and Labour in Electrohysterogram by Sample Entropy and Support Vector Machine
Discriminating Pregnancy and Labour in Electrohysterogram by Sample Entropy and Support Vector Machine
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样本熵和支持向量机在宫腔电图中区分妊娠和临产
DOI：
10.1166/jmihi.2017.2065
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发表时间：
2017-06-01
影响因子：
--
通讯作者：
Hao, Yaru
中科院分区：
文献类型：
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作者：
Chen, Lili;Hao, Yaru
早产（PTB）是围产期死亡率和长期发病率的主要原因。子宫电图（EHG）是一种非侵入性的实时技术，用于检测、诊断或预测与PTB相关的子宫收缩。在医院实践中，获得EHG记录，然后由临床医生目视检查子宫收缩信息，这是一项非常繁琐、耗时且成本高的任务。因此，对PTB的自动检测有很大的需求。提出了一种基于样本熵和遗传算法-支持向量机的宫缩自动识别方法。利用熵理论，计算每个样本16个通道的SampEn值，构造特征向量。然后，基于径向基函数（RBF）核的支持向量机的辨识模型。这些特征向量被送入SVM模型进行识别。为了提高辨识模型的训练速度和精度，设计了一种基于遗传算法（GA）的惩罚参数和RBF核函数参数最优值的获取方法。在评估识别模型的性能时，考虑了识别准确度、灵敏度和特异性。实验结果表明，与BP算法和SVM算法相比，GA-SVM的分类准确率和特异度都有较大提高，识别率达到95.20%，特异度达到96.14%，取得了较好的识别效果。最后，实验结果表明，本文提出的方法可以有效地识别子宫收缩的EHG。
Preterm birth (PTB) is a major cause of perinatal mortality and long-term morbidity. The Electrohysterogram (EHG) is non-invasive and a real-time technology used to detect, diagnose or predict the uterine contraction related to PTB. In hospital practice, EHG recordings are obtained and then visually inspected by clinicians for uterine contraction information, which is a very tedious, time-consuming and high-cost task. Therefore, there is a great demand for automatic detection of PTB. This paper presents a novel method for automatic uterine contraction identification based on sample entropy (SampEn) and genetic algorithm-support vector machine (GA-SVM). By using the entropy theory, SampEn values of 16 channels of each sample were calculated to construct feature vectors. Then the identification model was constructed based on SVM with radial basis function (RBF) kernel. These feature vectors were fed into the SVM model for identification. In order to improve the training speed and the accuracy of the identification model, this paper designs a method for obtaining optimal values of penalty parameter and RBF kernel function parameter based on genetic algorithm (GA). In assessing the performance of the identification model, identification accuracy, sensitivity and specificity were considered. Experimental results reveal that compared with the backpropagation (BP) algorithm and SVM, the classification performance of the GA-SVM is better in terms of classification accuracy and specificity which achieves a satisfying recognition results with accuracy of 95.20% and specificity of 96.14%. Finally, experimental results indicate that the method proposed in this work could be effective in identifying the uterine contraction by EHG.