Neural Networks and Fuzzy Logic Approximation and Prediction for HRV Analysis

The heart rate signal contains valuable information and its analysis has proven very useful in distinguishing healthy subject cardiograms from those of subjects with a variety of cardiac pathologies. The approach proposed here introduces a new use of neural network and fuzzy logic concepts for the prediction and approximation of cardiograms in order to differentiate between healthy and unhealthy subjects. Neural networks and fuzzy logic and even their hybrids have been applied in previous studies for the analysis of cardiogram data and their predictive/approximation capabilities are exploited in this study for cardiogram categorization. We show that measuring the prediction and approximation error of all methods, as they are applied to each cardiogram, results in a clear distinction between the two groups. This is in coherence with cardiac physiology, since the behaviour of a healthy subject ECG is more erratic than an unhealthy subject’s.