Riemann Liouvelle Fractional Integral Based Empirical Mode Decomposition for ECG Denoising

Electrocardiograph (ECG) denoising is the most important step in diagnosis of heart-related diseases, as the diagnosis gets influenced with noises. In this paper, a new method for ECG denoising is proposed, which incorporates empirical mode decomposition algorithm with Riegmann Liouvelle (RL) fractional integral filtering and Savitzky–Golay (SG) filtering. In the proposed method, noisy ECG signal is decomposed into its intrinsic mode functions (IMFs), from which noisy IMFs, corrupted with high-frequency (HF) and low-frequency (LF) noises, are identified by proposed noisy-IMFs identification methodologies. To denoise the signal, RL fractional integral filtering and SG filtering are applied on noisy IMFs corrupted with HF and LF noises, respectively; ECG signal is reconstructed with denoised IMFs and remaining signal dominant IMFs to obtain noise-free ECG signal. Proposed methodology is tested with MIT-BIH arrhythmia database. Its performance, in terms of signal-to-noise ratio and mean square error, is compared with other related ECG denoising methods based on fractional integral, empirical mode decomposition, and ensemble empirical mode decomposition. The obtained results by proposed method prove that the proposed method gives efficient noise removal performance.