Adaptive filters are increasingly popular in electrocardiogram noise cancellation due to their inherent ability to deal with nonstationary signals. In the past, adaptive filters with least mean square (LMS) algorithm and normalized LMS algorithm have been used for updating coefficients which is simple and provides satisfactory convergence performance. However, the LMS based adaptive filters use a long critical path to obtain the output. The critical path is generally reduced by pipelined structures with delay elements so that the desired sample period can be attained. In this paper, a delayed error normalized LMS adaptive filter is proposed to achieve high speed and low latency design with less computational elements. Simulation results show that the proposed technique provides better convergence performance with least mean square error.
ECG noise removal Adaptive filter Least mean square algorithm Mean square error (MSE)
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