Sensors & Transducers, Vol. 204, Issue 9, September 2016, pp. 21-28 21 Sensors & Transducers © 2016 by IFSA Publishing, S. L. http://www.sensorsportal.com A Sliding Window Empirical Mode Decomposition for Long Signals Algorithm 1 J. L. Sanchez, 2 Manuel D. Ortigueira, 3 Raul T. Rato, 4 Juan J. Trujillo 1 Departamento de Ingeniería Informática y de sistemas, Universidad de La Laguna 38271 La Laguna, Tenerife, Spain 2 UNINOVA and DEE of Faculdade de Ciências e Tecnologia da UNL, Caparica, Portugal 3 UNINOVA and Escola Superior de Tecnologia do Instituto Politécnico de Setubal, 2910-761 Setubal, Portugal 4 Departamento de Análisis Matemático, Universidad de La Laguna 38271 La Laguna, Tenerife, Spain 1 Tel.: 34922845043 1 E-mail: jsanrosa@ull.edu.es Received: 4 July 2016 /Accepted: 31 August 2016 /Published: 30 September 2016 Abstract: This document presents a sliding window algorithm for the calculation of the empirical mode decomposition for long signals. The spline calculation of very long signals requires a long computation time. Our aim is to improve the calculation time of the empirical mode decomposition for Long signals. Some authors have used sliding windows for the whole decomposition. Our main contribution is to reduce the computation time calculating each intrinsic mode function on a sliding window basis. That ensures the obtained intrinsic mode function has no discontinuities on the junction regions between consecutive windows. Moreover, the sliding window size changes adaptively according to the number of extrema in the previous intrinsic mode function. The effectiveness of the proposed method increases with the length of the signal obtaining computation times of the order of 30 % of the time required to obtain the decomposition using only a window as in the classical manner. Those results are important to apply the empirical mode decomposition to long signals. Particularly, to biomedical signals like long-term ECG or long term EEG. Copyright © 2016 IFSA Publishing, S. L. Keywords: Empirical mode decomposition, Intrinsic mode function, Long signals, Sliding window. 1. Introduction The Empirical Mode Decomposition (EMD), as was proposed initially by Huang, et al. [1] is a signal decomposition algorithm based on a successive removal of elemental signals: the Intrinsic Mode Functions (IMF). These are continuous functions such that at any point, the mean value of the envelope defined by the local maxima and the envelope defined by the local minima is zero. They are obtained through an iterative procedure called sifting that is a way of removing the dissymmetry between the upper and lower envelopes in order to transform the original signal into an amplitude modulated (AM) signal. Moreover, as the instantaneous frequency can change from instant to instant, it can be said that each IMF is a simultaneously amplitude and frequency modulated signal (AM/FM). So, the EMD is nothing else than a decomposition into a set of AM/FM modulated signals [2-5]. It must be emphasized that EMD is merely a computational algorithm that expresses a given signal http://www.sensorsportal.com/HTML/DIGEST/P_2856.htm