scholarly journals Empirical Mode Decomposition Techniques for Biomedical Time Series Analysis

2018 ◽  
Vol 99 ◽  
pp. 14-29 ◽  
Author(s):  
Keegan J. Moore ◽  
Mehmet Kurt ◽  
Melih Eriten ◽  
D. Michael McFarland ◽  
Lawrence A. Bergman ◽  
...  

2007 ◽  
Vol 7 (2) ◽  
pp. 299-307 ◽  
Author(s):  
J. Solé ◽  
A. Turiel ◽  
J. E. Llebot

Abstract. Determination of the timing and duration of paleoclimatic events is a challenging task. Classical techniques for time-series analysis rely too strongly on having a constant sampling rate, which poorly adapts to the uneven time recording of paleoclimatic variables; new, more flexible methods issued from Non-Linear Physics are hence required. In this paper, we have used Huang's Empirical Mode Decomposition (EMD) for the analysis of paleoclimatic series. We have studied three different time series of temperature proxies, characterizing oscillation patterns by using EMD. To measure the degree of temporal correlation of two variables, we have developed a method that relates couples of modes from different series by calculating the instantaneous phase differences among the associated modes. We observed that when two modes exhibited a constant phase difference, their frequencies were nearly equal to that of Milankovich cycles. Our results show that EMD is a good methodology not only for synchronization of different records but also for determination of the different local frequencies in each time series. Some of the obtained modes may be interpreted as the result of global forcing mechanisms.


2014 ◽  
Vol 602-605 ◽  
pp. 2330-2333 ◽  
Author(s):  
Jun Ma ◽  
Shi Hai Zhang

It is the precondition of vibration fault diagnosis technology that appropriate signal analysis method is applied to separate mechanical fault character message from vibration monitoring signal. Based on the characteristics of multi-exciting, multi-model, non-stationary, nonlinear of the complex mechanical vibration signal, the EMD (Empirical Mode Decomposition) method is firstly applied to decompose the most refined IMF (Intrinsic Mode Function) components of vibration monitoring signal, and then the time series analysis method is applied to estimate power spectrums of IMF components and separate the fault character messages. The feasibility and advantage of the associated method are proved by analyzing the diesel engine crankshaft vibration monitoring signal in the paper.


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