The Power of Tensor-Based Approaches in Cardiac Applications

  • Sibasankar PadhyEmail author
  • Griet Goovaerts
  • Martijn Boussé
  • Lieven De Lathauwer
  • Sabine Van Huffel
Part of the Series in BioEngineering book series (SERBIOENG)


The electrocardiogram (ECG) is a biomedical signal that is widely used to monitor the heart and diagnose cardiac problems. Depending on the clinical need, the ECG is recorded with one or multiple leads (or channels) from different body locations. The signals from different ECG leads represent the cardiac activity in different spatial directions and are thus complementary to each other. In traditional methods, the ECG signal is represented as a vector or a matrix and processed to analyze temporal information. When multiple leads are present, most methods process each lead individually and combine decisions from all leads in a later stage. While this approach is popular, it fails to exploit the structural information captured by the different leads. Recently, there is a trend towards the use of tensor-based methods in biomedical signal processing. These methods represent the signals by tensors, which are higher-order generalizations of vectors and matrices that allow the analysis of multiple modes simultaneously. In the past years, tensor decomposition methods have been applied to ECG signals to solve different clinical challenges. This chapter discusses the power of different tensor decompositions with a focus on typical ECG problems that can be solved using tensors.



This research was supported by: imec funds 2017, Belgian Foreign Affairs Development Cooperation: VLIR-UOS programs (20132019), Fonds de la Recherche Scientifique—FNRS and Fonds Wetenschappelijk Onderzoek—Vlaanderen under EOS Project no 30468160 (SeLMA), Research Council KU Leuven: C1 project C16/15/059-nD, European Research Council: The research leading to these results has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP7/2007–2013)/ERC Advanced Grant: BIOTENSORS (no. 339804). This paper reflects only the authors’ views and the Union is not liable for any use that may be made of the contained information. Griet Goovaerts is a SB Ph.D. fellow at Fonds voor Wetenschappelijk Onderzoek (FWO), Vlaanderen, supported by the Flemish government.


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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Sibasankar Padhy
    • 1
    • 2
    • 3
    Email author
  • Griet Goovaerts
    • 1
    • 2
  • Martijn Boussé
    • 1
  • Lieven De Lathauwer
    • 1
    • 4
  • Sabine Van Huffel
    • 1
    • 2
  1. 1.Department of Electrical Engineering (ESAT)STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU LeuvenLeuvenBelgium
  2. 2.imecLeuvenBelgium
  3. 3.School of Electronics (SENSE)Vellore Institute of TechnologyVelloreIndia
  4. 4.Group Science, Engineering and TechnologyKU Leuven KulakKortrijkBelgium

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