Highly accurate higher order statistics based neural network classifier of specific abnormality in electrocardiogram signals

The paper describes a simple yet highly accurate multilayer feed-forward neural network classifier (based on the backpropagation algorithm) specifically designed to successfully distinguish between normal and abnormal higher-order statistics features of electrocardiogram (EGG) signals. The concerned abnormality in ECG is associated with ventricular late potentials (LPs) indicative of life threatening heart diseases. The LPs are defined as signals from areas of delayed conduction which outlast the normal QRS period (80-100 msec). The QRS along with the P and T waves constitute the heart beat cycle. This classifier incorporates both preprocessing and adaptive weight adjustments across the input layer during the training phase of the network to enhance extraction of features pertinent to LPs found in 1-D cumulants. The latter is deemed necessary to offset the low S/N ratio in the cumulant domains concomitant to performing short data segmentation in order to capture the LPs transient appearance. We summarize the procedures of feature selection for neural network training, modification to the backpropagation algorithm to speed its rate of conversion, and the pilot trial results of the neural ECG classifier