Ensemble Empirical Mode Decomposition for Automated Denoising of Pulse Signals

Abstract

Pulse signals are often corrupted by noise, compromising signal integrity for downstream analysis. This paper presents an automated denoising technique for pulse waveforms using ensemble empirical mode decomposition (EEMD). The EEMD algorithm decomposes the signal into intrinsic mode functions (IMFs). Statistical metrics of IMF energy and entropy identify noise components for targeted removal via nonlinear filtering. Experiments on simulated pulse echoes demonstrated the approach of accurately eliminated noise regions. Compared to wavelet decomposition and Monte Carlo methods, the EEMD technique exhibited superior noise reduction and over 90% faster processing. This ensemble empirical mode decomposition approach provides an efficient, data-driven methodology for denoising pulse waveforms with applications in biomedical signal analysis.

Keywords

• ensemble empirical mode decomposition
• kurtosis detection
• ranking entropy
• signal denoising
• weak laser pulse signal