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.
The electrocardiograms (ECGs) record the electrical activity of the heart and are used to diagnose many heart disorders. This paper proposes a two-stage feed forward neural network for ECG signal classification. The research is aimed at the design of an intelligent ECG diagnosis tool that can recognise heart abnormalities while reducing the complexity, cost, and response time of the system. A number of neural network architectures are designed and compared for their ability to classify six different heart conditions. Two network architectures based on one stage and two stage feed forward neural networks are chosen for this investigation. The training and testing ECG signals are obtained from MIT-BIH database. The network inputs are comprised of 12 ECG features and 13 compressed components of each heart beat signal. The performance of the different modules as well as the efficiency of the whole system is presented. Among different architectures, a proposed multi-stage network named NETBST possesses the highest recognition rate of around 93%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems.
Hosseini et al. (Mon,) conducted a other in 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.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: