Abstract

Filtering the noise present in ECG signals by adaptive signal processing is the aim of the study. Adaptive digital filters are difficult to pipeline due to the presence of long feedback loops, careful calibration of step size and depth of pipelining. DLMS filters are designed to reduce the adaptation delay in the existing method. However with the LMS algorithm, the resulting rate of convergence is typically an order of magnitude slower than the RLS algorithm. The exponentially weighted RLS algorithm which converges in the mean square sense in about 2M iterations, where M is the number of taps in the transversal filter. The fine-grain structure of RLS and RRLS adaptive filters are designed. Signal to Noise Ratio (SNR) analysis for these filters are performed on a preliminary basis with different structures. Pipelined implementation of these adaptive filters yield higher throughput, higher sample rates and low power designs. The filter structures are designed and simulated in MATLAB SIMULINK. These structures are used for the noise cancellation in ECG signals.

Keywords:

ECG (Electrocardiography) signals, fine-grain structure, pipelining, RLS (Recursive Least Square) filter, VLSI (Very Large Scale Integration),

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Competing interests

The authors have no competing interests.

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The authors have no competing interests.