Fetal arrhythmia can manifest as irregular cardiac rhythm, abnormal heart rate or both irregular cardiac rhythm and abnormal heart rate. Fetal testing without proper care is very dangerous. Therefore, a multi-branch m...
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Fetal arrhythmia can manifest as irregular cardiac rhythm, abnormal heart rate or both irregular cardiac rhythm and abnormal heart rate. Fetal testing without proper care is very dangerous. Therefore, a multi-branch multi-scale convolutional neural network (MMSCNN) using automatic detection of fetal arrhythmia is proposed in this paper. Here, the input ECG signals are amassed from fetal ECG dataset. Then the input signals are preprocessed using the multivariate iterative filtering for removing noise and artifacts. Sparse regularization-based fuzzy C-means clustering and the pre-treated AECG signal sectioned into frames of 100-ms period are considered in the segmentation process. The classification method using multi-branch convolution neuralnetwork is classified as normal and arrhythmia. The weight parameter of the MMSCNN is optimized using bald eagle search optimization algorithm. The performance of the proposed method is analyzed with the help of metrics, such as accuracy, precision, ROC, F-score, specificity, recall and error rate analysis. Finally, the proposed MMSCNN-AD-FA method attains higher accuracy compared with existing methods.
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