This Project

     deals primarily with the effective utilization of adaptive filters used in the analysis of heart ECG and heart rate variability data. An adaptive filter can be simply defined as a filter that designs itself based on the characteristics of the input data. Adaptive filters become necessary when the desired response to a set of data cannot be classified based on the data's time and frequency characteristics alone, and heart ECG and heart rate variability are two examples of such a set of data. In this project, adaptive filters have been applied to the analysis of heart ECG because heartbeat detection, while relatively simple for the human mind and eye, is actually algorithmically somewhat complex, and non-adaptive filters applied to this set of data inevitably fail. Also in this project we have applied adaptive and non-adaptive filters to the analysis of heart rate variability, which is a topic of active medical research, because it gives a non-invasive and nearly-instantaneous examination of two major components of the central nervous system.