Feed Forward Neural Network Optimized Using PSO and GSA for the Automatic Classification of Heartbeat
نویسندگان
چکیده
In this paper, an automatic classifier has been developed using Feed Forward Neural Network (FFNN) to classify the ECG signals between different heartbeats. Here, the classifier is trained independently bymorphological, heartbeat interval features and temporal features using Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The trained classifier then classifies the beats into Normal beat (N), Premature Ventricular Contraction (PVC), Right Bundle Branch Block Beat (R), Fusion of Paced and Normal Beat (f), Fusion of Ventricular and Normal Beat (F) and the Atrial Premature Beat (A). The classifier performance is validated using the benchmark database such as MIT-BIH and the performance of the classifier trained independently using PSO and GSA is compared. It is observed that FFNN trained with GSA performs better than the one trained with PSO.
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