Abstract

This study investigates the effectiveness of three filtering methods—Butterworth IIR filter, Empirical Mode Decomposition (EMD), and Variational Mode Decomposition (VMD)—for noise removal from ECG signals across 17 arrhythmia classes using the MIT-BIH Arrhythmia Database. The methods were evaluated using statistical metrics, including correlation coefficient, Mean Square Error (MSE), and Signal-to-Noise Ratio (SNR). The results demonstrate that VMD outperforms the other methods with the highest correlation (0.9838), the lowest MSE (0.0005), and the highest SNR (25.2979), indicating its superior ability to preserve signal quality and remove noise effectively. The EMD method exhibited the lowest performance with higher MSE and lower SNR. This study highlights the potential of VMD as a reliable and consistent signal processing technique for ECG analysis.

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