An attention-based CNN-BiLSTM hybrid neural network enhanced with features of discrete wavelet transformation for fetal acidosis classification

作者：

Highlights：

• CNN-BiLSTM can integrate spatial features and temporal correlation of FHR signals.

• Attention is focused on more relevant features for fetal acidosis classification.

• DWT can reduce the overfitting and improve the accuracy of model classification.

摘要

•CNN-BiLSTM can integrate spatial features and temporal correlation of FHR signals.•Attention is focused on more relevant features for fetal acidosis classification.•DWT can reduce the overfitting and improve the accuracy of model classification.

论文关键词：Cardiotocography monitoring,Fetal acidosis,Deep learning,Discrete wavelet transformation features,Sigmoid classification

论文评审过程：Received 28 August 2020, Revised 1 August 2021, Accepted 2 August 2021, Available online 5 August 2021, Version of Record 10 August 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115714