Cardiotocography signal abnormality classification using time-frequency features and Ensemble Cost-sensitive SVM classifier. (March 2021)

• Record Type: Journal Article
• Authors: Zeng, Rongdan; Lu, Yaosheng; Long, Shun; Wang, Chuan; Bai, Jieyun
• Is Part Of: Computers in biology and medicine. Volume 130(2021)
• Publication Date: 2021-03
• Subjects: Abnormality classification -- Cardiotocography -- Fetal heart rate -- Time-frequency representation -- Wavelet; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104218
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: New representations are used to describe fetal heart rate and uterine contraction. New time-frequency features achieved good results in classifying Cardiotocography. New ECSVM classifier improves the classification performance in imbalanced datasets. The method reduces the false positive rate while ensuring a high true positive rate. Results show that the method has an increase in accurately detecting abnormal fetuses.