Artificial neural network based electrocardiography analyzer

At the present scenario, one of the main reasons of death is cardiovascular related diseases. This project proposes a system to help the doctor to detect cardiac arrhythmia. The techniques used in this pattern recognition comprise: signal pre-processing, filter design analysis and neural network toolboxes in Matlab environment. Normal and disease ECG signals have been tested by using recordings obtained from the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) arrhythmia database. Supervised Radial Basis Function (RBF) network architecture is used for filtration, extraction of morphological features of ECG signal and for classification of abnormal cardiac conditions. Different threshold techniques were implemented for best detection of fudicial points on the derivative of the filtered ECG signal through signal processing. All fudicial points and intervals extracted were learned to the RBF network for extraction of features through the overall record which is further processed to ANN for classification. The designed network gives very minimum error in terms of 10^-15.