An empirical wavelet transform based approach for multivariate data processing application to cardiovascular physiological signals

• Autorzy: Singh O. , Sunkaria R. K.

Identyfikatory

DOI

10.1515/bams-2018-0030

• Języki publikacji: EN

Abstrakty

EN

Background: This article proposes an extension of empirical wavelet transform (EWT) algorithm for multivariate signals specifically applied to cardiovascular physiological signals. Materials and methods: EWT is a newly proposed algorithm for extracting the modes in a signal and is based on the design of an adaptive wavelet filter bank. The proposed algorithm finds an optimum signal in the multivariate data set based on mode estimation strategy and then its corresponding spectra is segmented and utilized for extracting the modes across all the channels of the data set. Results: The proposed algorithm is able to find the common oscillatory modes within the multivariate data and can be applied for multichannel heterogeneous data analysis having unequal number of samples in different channels. The proposed algorithm was tested on different synthetic multivariate data and a real physiological trivariate data series of electrocardiogram, respiration, and blood pressure to justify its validation. Conclusions: In this article, the EWT is extended for multivariate signals and it was demonstrated that the component-wise processing of multivariate data leads to the alignment of common oscillating modes across the components.

Słowa kluczowe

EN

empirical mode decomposition; EMD; empirical wavelet transform; EWT; multivariate empirical mode decomposition; MEMD; multivariate signal processing

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2018
• Tom: Vol. 14, no. 4
• Strony: art. no. 20180030
• Opis fizyczny: Bibliogr. 9 poz., rys.

Twórcy

autor

Singh O.

• Department of Electronics and Communication Engineering, National Institute of Technology, Srinagar 190006, India

autor

Sunkaria R. K.

• Department of Electronics and Communication Engineering, National Institute of Technology, Jalandhar 144011, India

Bibliografia

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• [2] Wu Z, Huang NE. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 2009;1:1-41.
• [3] Hu M, Liang H. Adaptive multiscale entropy analysis of multivariate neural data. IEEE Trans Biomed Eng 2012;59:12-5.
• [4] Rehman NU, Mandic DP. Multivariate empirical mode decomposition. Proc R Soc Lond A 2010;466:1291-302.
• [5] Rilling G, Flandrin P, Goncalves P, Lilly JM. Bi-variate empirical mode decomposition. IEEE Signal Process Lett 2007;14;936-9.
• [6] Rehman NU, Mandic DP. Empirical mode decomposition for trivariate signals. IEEE Trans Signal Process. 2010;58:1059-68.
• [7] Rehman NU, Mandic DP. Filter bank property of multivariate empirical mode decomposition. IEEE Trans Signal Process 2011;59:2421-6.
• [8] Gilles J. Empirical wavelet transform. IEEE Signal Process 2013;61:3999-4010.
• [9] Baílon R, Sörnmo L, Laguna P. A robust method for ECG-based estimation of the respiratory frequency during stress testing. IEEE Trans Biomed Eng 2006;53:1273-85.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).