Inclusion of ECG and EEG analysis in neural network models

Evaluation of biomedical signals is important in the diagnosis of numerous diseases, chiefly in cardiology through the use of electrocardiograms, and to a more limited extent, in neurology through the use of electroencephalograms. While automated techniques exist for both ECG and EEG analysis, it is likely that additional information can be extracted from these signals through the use of new methods. A chaotic method for analysis of signal analysis variability is presented here that identifies the degree of variability in the signal over time. A second focus is to develop higher order decision models that can incorporate these results with other clinical parameters to represent a more comprehensive view of the disease state, using a neural network model.