Key result
DFA-SDCNN-GHANet improves fetal arrhythmia detection F1-scores by up to ~10% versus existing methods.
Why the study?
Fetal arrhythmia is a critical medical condition associated with perinatal morbidity and cardiac complications.
Does DFA-SDCNN-GHANet improve the detection of fetal arrhythmia from ECG signals compared to existing methods?
Does DFA-SDCNN-GHANet improve the detection of fetal arrhythmia from ECG signals compared to existing methods?
The proposed DFA-SDCNN-GHANet framework improves the detection accuracy of fetal arrhythmias from ECG signals compared to existing methods.
Hypothesis-generating for automated fetal arrhythmia detection; leaves open prospective clinical validation before any practice change.
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.
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T. et al. (2026) studied Fetal arrhythmia. DFA-SDCNN-GHANet (Spatial Deep Convolutional Neural Network with GHA-DenseNet) vs. existing methods was evaluated on F1-score. The proposed DFA-SDCNN-GHANet framework for detecting fetal arrhythmia achieved 6.62%, 7.94%, and 9.75% higher F1-scores compared to existing methods.
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