Monitoring of Cardiac Arrhythmia Patterns by Adaptive Analysis

In this paper, a study and development of a monitoring adaptive system based on dynamic quadratic neural unit are presented. The system is trained with a recurrent learning method, sample-by-sample in real time. This model will help to the prediction of possible cardiac arrhythmias in patients between 23 to 89 years old, age range of the electrocardiogram signals obtained from the Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database. By means of the implementation of this adaptive monitoring system the model is capable of processing heart rate signals in real time and to recognize patterns that predict cardiac arrhythmias up to 1 second ahead. The Dynamic Quadratic Neural Unit in real time has demonstrated presenting greater efficiency and precision comparing with multilayer perceptron-type neural networks for pattern classification and prediction; in addition, this architecture has demonstrated in developed research, to be superior to other different type of adaptive architectures.