Design and Development of Improved Coyote Optimization-based Adaptive Equalization Technique for ECG Signal Transmission

Abstract

Biological signals such as an electrocardiogram (ECG) are broadcast directly through wireless devices and mobile networks during patients' real-time activities and over limitless distances. However, the transmission of signals through a limited band channel or over multi-propagation suffers from limitations such as inter-symbol interference (ISI). In channel output, the adjacent symbols smudge and integrate with each other by degenerating during error analysis. Equalization filters are used to improve such kinds of deformation. A new adaptive equalization approach is proposed in this paper to extract real broadcasted signals from Gaussian noise-distorted ECG signals using the enhanced meta-heuristic algorithm. The weight optimization strategy of the "adaptive equalization technique" is implemented using oppositional searched coyote optimization algorithm (OS-COA). The main purpose of adapting the improved meta-heuristic-based adaptive equalization technique is to decrease error in the section of receivers, thus ensuring the received ECG signal is error-free. The obtained outcomes are examined to evaluate the performance of error metrics like "mean square error (MSE), and convergence rate" of the introduced model and for comparison with other existing equalization approaches. According to the experimental evaluation, the recommended adaptive linear equalization technique has better extraction performance than other blind and nonlinear equalization approaches.