Abstract
Decomposing non-stationary signals using Empirical Mode Decomposition (EMD) highly facilitates signal analyses and processing. According to the original algorithm, EMD decomposes the input signal into useful Intrinsic Mode Functions (IMFs). However, EMD has some drawbacks. The first one is that the number of IMFs is not known in advance and can change with a small variation in the data. The second one is that EMD must be applied to a signal in its entirety, being not possible to proceed by parts either to reduce the computational load or to deal with real-time data. So, EMD it’s not feasible to use on long signals or for dealing with streaming data signals. These two drawbacks limit EMD practical application and are addressed in this text. A novel way to run EMD on any long or streaming signal is provided, while maintaining a constant number of IMF outputs. The method uses an innovative extension of the original EMD, called Flow Empirical Mode decomposition (FEMD), which applies EMD on sliding windows and ensures a fixed number of IMFs. Furthermore, it is demonstrated the successful use of FEMD on an Electrocardiogram (ECG) analyses.
The developed FEMD software was made freely available.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Code available at https://matlab.com/FEMD.
- 2.
MIT ECG Dataset can be found at https://archive.physionet.org/physiobank/database/stdb/.
References
Almeida, R., Gouveia, S., Rocha, A.P., Pueyo, E., Martinez, J.P., Laguna, P.: Automatic ECG characterization: application to the QT interval variability. IEEE Trans. Biomed. Eng. 53(7), 1317–1329 (2006)
Amarjeet, Chhabra, J.K.: FP-ABC: fuzzy-Pareto dominance driven artificial bee colony algorithm for many-objective software module clustering. Computer Languages, Systems and Structures (2018)
Box, G.E.P., Meyer, R.D.: An analysis for unreplicated fractional factorials. Technometrics 28, 11–18 (1986)
Chanarin, N., Caplin, J., Peacock, A.: Deep learning approach for active classification of electrocardiogram signals. Inf. Sci. 345, 340–354 (2016)
Chanarin, N., Caplin, J., Peacock, A.: Clinical outcomes of surgical or transcatheter aortic-valve replacement in intermediate-risk patients stratified by STS score: results from the SURTAVI trial. J. Am. College Cardiol. (2017)
Chen, W.: Electrocardiogram. In: Tamura, T., Chen, W. (eds.) Seamless Healthcare Monitoring, pp. 3–44. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-69362-0_1
Davey, P., Sharman, D.: The electrocardiogram. Medicine (United Kingdom) (2018)
de Santiago, L.: Empirical mode decomposition processing to improve multifocal-visual-evoked-potential signal analysis in multiple sclerosis. PLOS ONE 13(4), e0196928 (2018)
Divorra, O., Granai, L., Lemay, M., Hernandez, J.M., Vandergheynst, P., Vesin, J.-M.: Ventricular and atrial activity estimation through sparse ECG signal decompositions. In: 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 2, pp. II–III. IEEE (2006)
Flandrin, P., Rilling, G., Goncalves, P.: Empirical mode decomposition as a filter bank. IEEE Signal Process. Lett. 11(2), 112–114 (2004)
Grobman, A.B.: On Research Collections. Copeia (2006)
Hadamard, J.: Sur les problèmes aux dérivées partielles et leur signification physique. Princeton university bulletin, pp. 49–52 (1902)
Hasan, M.A., Chauhan, V.S., Krishnan, S.: Beat-to-beat T-wave alternans detection using the ensemble empirical mode decomposition method. Comput. Biol. Med. 77, 1–8 (2016)
Huang, N.E., et al.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. London. Series A: Math. Phys. Eng. Sci. 454(1971), 903–995 (1998)
Xue, Y.J., Zhang, J., Chang, Q., Zhang, L.P., Zou, F.: Instantaneous frequency extraction using the EMD-based wavelet ridge to reveal geological features. Frontiers in Earth Science, 6, May 2018
Karatoprak, S., Seker, E.: An improved empirical mode decomposition method using variable window median filter for early fault detection in electric motors. Math. Probl. Eng. 2019, 1–9 (2019)
Li, X.Y., Wang, T., Zhou, P., Feng, H.Q.: St-t complex automatic analysis of the electrocardiogram signals based on wavelet transform. In: 2003 IEEE 29th Annual Proceedings of Bioengineering Conference, pp. 144–145. IEEE (2003)
Preußand Ch., P.M.: Current cardiologic interventions in ventricular arrythmias. Internistische Praxis (2017)
Mallot, H.A., Basten, K.: Biological and artificial systems. Image and Vision Computing, Embodied spatial cognition (2009)
Manjula, M., Sarma, A.V.R.S.: Comparison of empirical mode decomposition and wavelet based classification of power quality events. Energy Procedia 14, 1156–1162 (2012)
McSharry, P.E., Clifford, G.D., Tarassenko, L., Smith, L.A.: Electrocardiogram signals. IEEE Trans. Biomed. Eng. (2003)
Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine (2001)
Rato, R.T., Ortigueira, M.D., Batista, A.G.: On the HHT, its problems, and some solutions. Mechanical Systems and Signal Processing (2008)
Rilling, G., Flandrin, P., Goncalves, P.: On empirical mode decomposition and its algorithms. In: IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, vol. 3, pp. 8–11. NSIP-03, Grado (I) (2003)
Sanchez, J.L., Ortigueira, M.D., Rato, R.T., Trujillo, J.J.: A sliding window empirical mode decomposition for long signals algorithm. Sens. Transducers 204(9), 21 (2016)
Sanchez, J.L., Ortigueira, M.D., Rato, R.T., Trujillo, J.J.: An improved empirical mode decomposition for long signals. SIGNAL 2016 Editors, p. 65 (2016)
Shanbhag, D.N., Arnold, B.C.: Pareto distributions. J. Roy. Stat. Soc. Series A (Stat. Soc.) (2006)
Tarascio, V.J.: Pareto’s methodological approach to economics: A study in the history of some scientific aspects of economic thought. Studies in economics and business administration. University of North Carolina Press, Chapel Hill, NC (1968)
Weng, B., Blanco-Velasco, M., Barner, K.E.: Baseline wander correction in ECG by the empirical mode decomposition. In: Proceedings of the IEEE 32nd Annual Northeast Bioengineering Conference, pp. 135–136. IEEE (2006)
Zhang, C., Li, Z., Hu, C., Chen, S., Wang, J., Zhang, X.: An optimized ensemble local mean decomposition method for fault detection of mechanical components. Measur. Sci. Technol. 28(3), 035102 (2017)
Zhou, P., Lock, B., Kuiken, T.A.: Real time ECG artifact removal for myoelectric prosthesis control. Physiol. Measur. 28(4), 397–413 (2007)
Acknowledgments
This work is mainly supported by the project UIDB/04111/2020. Furthermore, this article was also supported by the ECSEL Joint Undertaking (JU) under grant agreement No 783221. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Belgium, Czech Republic, Finland, Germany, Greece, Italy, Latvia, Norway, Poland, Portugal, Spain, Sweden. This project has also received funding from SECREDAS, which has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement nr.783119. This work was also supported by Portuguese Agency “Fundação para a Ciência e a Tecnologia” (FCT), in the framework of project PEST UIDB/00066/2020.
The authors would like to thank India Gouveia for the design help on some of the graphical work. Also the authors would like to thank MIT-BIH for having theirs databases available and of free access. Finally, the authors would like to thank the PDMFC Research Group, in particularly Luis Miguel Campos.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pedro, D., Rato, R.T., Matos-Carvalho, J.P., Fonseca, J.M., Mora, A. (2022). Flow Empirical Mode Decomposition. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-82199-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-82199-9_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82198-2
Online ISBN: 978-3-030-82199-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)