Abstract
The Electro Cardiogram (ECG) is most generally used for the identification and conclusion of heart illnesses. Great quality ECGs are used by doctors for understanding and identifying physiological and neurotic peculiarities. Nonetheless, in real circumstances, ECG signals are undermined by the corruption of artifcats. Though the LMS algorithm is mostly used for the removal of noise to enhance ECG, some issues persist such as the higher input data vector. So, to overcome such limitations, a logarithmic adaptive filtering technique is been used for ECG enhancement. Logarithmic algorithms incorporate the lower and higher order values of the error into a single continuous updating equation since they are dependent on the error amount. This result in the surge of convergence rate in logarithmic adaptive algorithms similar to the LMLS algorithm and reduces the complexity of computations in a conventional LMS algorithm. Experiment results are performed over various state-of-art models in the literature review under various measures and the proposed model outperforms effectively.
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