ECG Signal Denoising Using Sparrow Search Algorithm

The Electrocardiogram (ECG) is a wave portrayal of the electrical representation of heart function that offers important details of the heart's rhythms. It diagnoses cardiac problems by detecting the heart's electrical functioning. While recording the original ECG signal, some noise is added. Therefore, it is rational to rebuild the signal by reducing noise. This work describes an ECG signal denoising technique that employs the sparrow search algorithm (SSA) and is based on the finite impulse response (FIR) filtering approach. Performance measures such as mean difference (MD), normalized root mean square error (NRMSE), signal-to-noise ratio (SNR), mean square error (MSE) and correlation coefficient (CC) are computed to validate the findings. The experimental results demonstrate that using SSA has obtained better performance compare to other method.