Ensemble feature extraction-based prediction of fetal arrhythmia using cardiotocographic signals

Abstract

The classification of Cardiotocography (CTG) signal abnormalities is critical in the identification of fetal anomalies. The non-stationary nature of CTG, as well as the dataset asymmetry, make this classification task complicated. Manual CTG analysis interpretation can easily result in a low diagnosis rate, which is mainly caused by a variety of subjective variables. Hence, in this research a new Ensemble Feature Extraction (EFE) model is implemented to analyze the nonlinear cardiotocographic signals. First, the collected signal artifacts are removed with interpolation operation, then down sampled to eliminate the complexity during classification. Followed by, a new EFE module is applied to extract the features, here learning model SE-ResNet-50 (Squeeze and Excitation -ResNet-50) is applied to separate the features from the regular and Time-Frequency data transformed (via CWT transform) signal. Wavelet Packet Transform is exploited to extract T-F signals. Then the enhanced feature set dimension is lessened by using Linear Discriminant Analysis (LDA) model. Finally, the features are fused and fed to the XG-booster for classification, and the patient is advised to seek therapy based on the outcome. The proposed method achieved 96.34% accuracy, 96.7% precision, 97.3% recalls, and 95.64% Quality Index (QI) on two-class classifications. This ensemble feature extraction increases the classification results and proves very advantageous to cardiotocographic signal classification. The results of the experiments show how effective the suggested EFE is.

Keywords

• Ensemble feature extraction
• Interpolation
• Linear discriminative analysis
• SE-ResNet-50
• XG-Booster
• Feature fusion