Recognition of ECG Arrthymia : using Backpropagation Neural Network

Neural network have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to processing of temporal data has been restricted in several places. Here in this research work the technique that is being used, adds the dimensions of time to the well known Back-Propagation neural network algorithm. In space time neural network, the synaptic weights between two artificial neurons are replaced with an adaptable - adjustable filter. Instead of single synaptic weight, it provides a plurality of weights, which represents not only association but also temporal dependencies. Now the synaptic weights of the neural network are the coefficients of the adaptable digital filter, which gives a neural network that is defined both spatially and temporally. This network is a Time-Delayed neural network and is also known as Elman network. In this present book, an attempt is made to design a Spatio-Temporal artificial neural network which is used to classify arrhythmia. In this book a neural network is developed using back propagation algorithm and is tested for the classification of arrhythmia.