Advanced detection of fetal arrhythmia utilizing spatial deep convolutional neural network with GHA-DenseNet for enhanced electrocardiographic signal processing.

Fetal arrhythmia is a critical medical condition associated with perinatal morbidity and cardiac complications. This paper proposes an Advanced Detection of Fetal Arrhythmia utilizing Spatial Deep Convolutional Neural Network with GHA-DenseNet (DFA-SDCNN-GHANet). Initially, fetal ECG signals are preprocessed using Adaptive Fast Desensitized Kalman Filter (AFDKF) to remove artefacts, followed by Synthetic Minority Over-sampling Technique (SMOTE) for class balancing. Iterative Local Maximum Synchrosqueezing-Extracting Transform (ILMSET) extracts informative features, which are classified using SDCNN-GHANet optimized by the Black-Winged Kite Algorithm (BWKA). The proposed framework achieved 6.62%, 7.94%, and 9.75% higher F1-score than existing methods.