ECG signal denoising and baseline wander correction based on the empirical mode decomposition

By M. Blanco–Velasco, Binwei Weng, K. Barner. Comput. Biol. Medicine, 2008

693 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/57e91cd79527e7bcf2c73be572b9baca40c5b236

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Signal Processing

1. Mu-Chen Chen
2. Yu Wei

Citations in the corpus

• Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks (2012, background): “As stated in Brooks (2008), ARIMA performs well and robustly in modeling linear and stationary time series.”
• Successive variational mode decomposition (2020, neutral): “EMD was satisfactorily used in many applications such as extracting the vital signs from biological signals [2–5] , removing interferences from signal [ 6 , 7 ], and climate analysis [ 8 , 9 ].”
• Review of noise removal techniques in ECG signals (2020, methodology): “[36] have concluded after an analysis of EMD on clean and noisy ECG that it is possible to decontaminate the noisy signal and at the same time preserve the QRS complex by applying temporal processing in the EMD domain, which otherwise is difficult due to the similar spectral presence of both noise a”