Background

Cardiotocography (CTG) signal abnormality classification plays an important role in the diagnosis of abnormal fetuses. This classification problem is made difficult by the non-stationary nature of CTG and the dataset imbalance. This paper introduces a novel application of Time-frequency (TF) features and Ensemble Cost-sensitive Support Vector Machine (ECSVM) classifier to tackle these problems.

Methods

Firstly, CTG signals are converted into TF-domain representations by Continuous Wavelet Transform (CWT), Wavelet Coherence (WTC), and Cross-wavelet Transform (XWT). From these representations, a novel image descriptor is used to extract the TF features. Then, the linear feature is derived from the time-domain representation of the CTG signal. The linear and TF features are fed to the ECSVM classifier for prediction and classification of fetal outcome.

Results

The TF features show the significant difference (p-value<0.05) in distinguishing abnormal CTG signals, but not for traditional nonlinear features. In ECSVM abnormality classification, using only linear features, the sensitivity, specificity, and quality index are 59.3%, 78.3%, and 68.1%, respectively, whereas more effective results (sensitivity: 85.2%, specificity: 66.1%, and quality index: 75.0%) are obtained using a combination of linear and TF features, with a performance improvement index of 10.1%. Especially, the area under the receiver operating characteristic curve (0.77 vs. 0.64) is significantly increased with the ECSVM vs. SVM.

Conclusion

Our method can greatly improve the classification results, especially for sensitivity. It improves the true positive rate of CTG abnormality classification and reduces the false positive rate, which may help detect and treat abnormal fetuses during labor.

中文翻译：

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使用时频特征和集成成本敏感型SVM分类器进行心动图信号异常分类

背景

心电图（CTG）信号异常分类在异常胎儿的诊断中起着重要作用。CTG的非平稳性质和数据集不平衡使得此分类问题变得困难。本文介绍了时频（TF）功能和集成成本敏感支持向量机（ECSVM）分类器的新颖应用，以解决这些问题。

方法

首先，CTG信号通过连续小波变换（CWT），小波相干性（WTC）和交叉小波变换（XWT）转换为TF域表示。从这些表示中，使用新颖的图像描述符来提取TF特征。然后，从CTG信号的时域表示中得出线性特征。线性和TF特征被馈送到ECSVM分类器，以进行胎儿结局的预测和分类。

结果

TF特征在区分异常CTG信号方面显示出显着差异（p值<0.05），但对于传统的非线性特征却没有。在ECSVM异常分类中，仅使用线性特征，灵敏度，特异性和质量指数分别为59.3％，78.3％和68.1％，而更有效的结果（灵敏度：85.2％，特异性：66.1％和质量指数：结合使用线性和TF特征可获得75.0％的性能，性能改进指数为10.1％。尤其是，随着ECSVM与SVM的关系，接收器工作特性曲线下的面积（0.77与0.64）显着增加。

结论

我们的方法可以大大提高分类结果，特别是对于灵敏度。它可以提高CTG异常分类的真实阳性率，并减少错误阳性率，这可能有助于检测和治疗分娩期间的异常胎儿。