The Power of Low Rank Tensor Approximations in Smart Patient Monitoring

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Hippocrates August 31, 2017 8:40 am - 9:40 am

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Prof. Sabine Van Huffel
(Click Image for speaker Bio)

Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Belgium

An overview of applications in Smart Diagnostics is presented in which low rank tensor approximations emerge into their computational core.

Accurate and automated extraction of clinically relevant diagnostic information from patient recordings requires an ingenious combination of adequate pretreatment of the data (e.g. artefact removal), feature selection, pattern recognition, decision support, up to their embedding into user-friendly user  interfaces.
The underlying computational problems can be solved by making use of low rank tensor approximations  as building blocks of higher-level signal processing algorithms. A major challenge here is how to make these mathematical decompositions  “interpretable” such that they reveal the underlying clinically relevant information and improve medical diagnosis. The addition of relevant constraints and source models can help to achieve this. Multimodal data fusion poses additional challenges on how to couple the associated tensor decompositions thereby imposing appropriate constraints translating underlying relationships and common dynamics.

The application of these low rank tensor approximations and their benefits will be illustrated in a variety of case studies. In particular, their emerging power in cardiac monitoring using multilead Electrocardiography (ECG) will be shown in T-wave alternans and irregular heartbeat detection. In addition, their added value in Magnetic Resonance Spectroscopic Imaging combined with Magnetic Resonance Imaging is shown to improve brain tumour recognition. Although Canonical Polyadic Decompositions (CPD) and Multilinear Singular Value Decompositions (MLSVD) are most popular, their extensions are emerging in clinical applications, e.g. Block Term Decompositions (BTD) and multiscale MLSVD, as well as coupled tensor decompositions.

In conclusion, tensor decompositions can be highly relevant in biomedical data processing.
Nevertheless, their use in smart diagnostics is still largely unexplored.

Presentation Slides

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