The use of data profiles in class 2 and 3 adaptive filters

Adaptive filters are optimum filters whose transfer functions adapt to changing input statistics. Such adaptation is usually needed because of fluctuating signal and/or noise conditions. These filters can be classified in order of increasing difficulty based upon the a priori assumptions about the signal and noise models. A class 2/3 adaptive filter is a transitional filter between class 2 and class 3 filters. In this paper, we use data profiles to formulate class 2/3 filters. Averaging provides an estimate of the signal and eliminates noise. Data profiles can also be used to formulate either class 2 and/or class 3 filters. Results show that this method provides improved estimates of expected signal and/or noise by smoothing the average signal profile. To demonstrate this approach, these results are used to process EKG signals where no a priori information is available