A Learning Based Widrow-Hoff Delta Algorithm for Noise Reduction in Biomedical Signals
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Efficient Artifact Elimination in Cardiac Signals using Variable Step Size Adaptive Noise Cancellers
International Journal of Measurement Technologies and Instrumentation Engineering
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We analyze the steady-state mean square error (MSE) convergence of the LMS algorithm when deterministic functions are used as reference inputs. A particular adaptive linear combiner is presented where the reference inputs are any set of orthogonal basis functions-the adaptive orthogonal linear combiner (AOLC). Several authors have applied this structure always considering in the analysis a time-average behavior over one signal occurrence. We make a more precise analysis using the deterministic nature of the reference inputs and their time-variant correlation matrix. Two different situations are considered in the analysis: orthogonal complete expansions and incomplete expansions. The steady-state misadjustment is calculated using two different procedures with equivalent results: the classical one (analyzing the transient behavior of the MSE) and as the residual noise at the output of the equivalent time-variant transfer function of the system. The latter procedure allows a very simple formalism being valid for colored noise as well. The derived expressions for steady-state misadjustment are contrasted with experimental results in electrocardiographic (ECG) signals, giving exact concordance for any value of the step size