Windowed State-Space Filters for Signal Detection and Separation

Wildhaber, Reto A.; Zalmai, Nour; Jacomet, Marcel; Loeliger, Hans-Andrea (2018). Windowed State-Space Filters for Signal Detection and Separation IEEE Transactions on Signal Processing, 66(14), pp. 3768-3783. 10.1109/TSP.2018.2833804

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This paper introduces a toolbox for model-based detection, separation, and reconstruction of signals that is especially suited for biomedical signals, such as electrocardiograms (ECGs) or electromyograms (EMGs). The modeling is based on autonomous linear state space models (LSSMs), which are localized with flexible windows. The models are fit to observations by minimizing the squared error while the use of LSSMs leads to efficient recursive error computations and minimizations. Multisection windows enable complex models, and per-sample weights enable multistage processing or adaptive smoothing. This paper is motivated by, and intended for, practical applications, for which several examples and tabulated cost computations are given.

Item Type:	Journal Article (Original Article)
Division/Institute:	School of Engineering and Computer Science > Institute for Human Centered Engineering (HUCE) School of Engineering and Computer Science BFH Centres and strategic thematic fields > BFH centre for Health technologies
Name:	Wildhaber, Reto A.; Zalmai, Nour; Jacomet, Marcel and Loeliger, Hans-Andrea
Subjects:	R Medicine > R Medicine (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
ISSN:	1053-587X
Language:	English
Submitter:	Marcel Jacomet
Date Deposited:	12 Feb 2020 09:00
Last Modified:	12 Feb 2020 09:00
Publisher DOI:	10.1109/TSP.2018.2833804
ARBOR DOI:	10.24451/arbor.8539
URI:	https://arbor.bfh.ch/id/eprint/8539