Implementation of Neural Networks Based ECG classifi´er on TMS320C6711 processor

This paper presents the implementation of near optimal electrocardiogram (ECG) classifier based on multilayer perceptron neural networks (MLP NN). In the present investigations the optimized MLP NN based classifier is designed and implemented for detection of normal and abnormal ECG. Some dominant unique features of ECG are extracted using digital signal processing tools to optimize the MLP NN model. For this, MLP NN network is used to maximize accuracy under the constraints of minimum network dimension so that its hardware implementation further requires minimum number of components to satisfy real time constraints and low power consumption. The classification accuracy of MLP NN is found very good even after repeating the simulation experiments a number of times on different data partitions. The MLP NN thus designed has been implemented on the TMS320C6711 processor.