Detection of preterm birth in electrohysterogram signals based on wavelet transform and stacked sparse autoencoder
Detection of preterm birth in electrohysterogram signals based on wavelet transform and stacked sparse autoencoder
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DOI：
10.1371/journal.pone.0214712
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发表时间：
2019-04-16
期刊：
影响因子：
3.7
通讯作者：
Hu, Xue
中科院分区：
文献类型：
--
作者：
Chen, Lili;Hao, Yaru;Hu, Xue
基于子宫肌电图，本文设计了一种利用基于小波的非线性特征和堆叠稀疏自编码器的早产检测新方法。对于每个样本，执行时间序列的三级小波分解。提取了第 3 级的近似系数和第 1、2 和 3 级的细节系数。计算第 1、2、3 级细节系数和第 3 级近似系数的样本熵作为特征。该分类器是基于堆叠稀疏自动编码器构建的。此外，还进一步将堆叠稀疏自动编码器与极限学习机和支持向量机的子宫电图分类性能进行了比较。实验结果表明，基于堆叠稀疏自动编码器的分类器比其他两种分类器表现出更好的性能，准确率为90%，灵敏度为92%，特异性为88%。结果表明，本文提出的方法可以有效地通过子宫电图检测早产，并且本文设计的框架比其他技术具有更高的辨别力。
Based on electrohysterogram, this paper designed a new method using wavelet-based nonlinear features and stacked sparse autoencoder for preterm birth detection. For each sample, three level wavelet decomposition of a time series was performed. Approximation coefficients at level 3 and detail coefficients at levels 1, 2 and 3 were extracted. Sample entropy of the detail coefficients at levels 1, 2, 3 and approximation coefficients at level 3 were computed as features. The classifier was constructed based on stacked sparse auto encoder. In addition, stacked sparse autoencoder was further compared with extreme learning machine and support vector machine in relation to their classification performance of electrohysterogram. The experiment results reveal that classifier based on stacked sparse autoencoder showed better performance than the other two classifiers with an accuracy of 90%, a sensitivity of 92%, a specificity of 88%. The results indicate that the method proposed in this paper could be effective for detecting preterm birth in electrohysterogram and the framework designed in this work presents higher discriminability than other techniques.