Key result
A proposed multi-stage feed forward neural network named NET_BST achieved a recognition rate of around 93% for classifying six different heart conditions from ECG signals.
A proposed multi-stage feed forward neural network (NET_BST) achieved a 93% recognition rate for classifying ECG abnormalities, demonstrating potential utility for automated ECG diagnosis.
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May support AI ECG tools in low-resource settings; leaves open prospective validation before clinical use.
Hosseini et al. (2005) studied Heart abnormalities. Multi-stage feed forward neural network (NET_BST) vs. Other neural network architectures was evaluated on Recognition rate for classifying six different heart conditions. A proposed multi-stage feed forward neural network named NET_BST achieved a recognition rate of around 93% for classifying six different heart conditions from ECG signals.
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