Windowed state-space filters for signal detection and separation
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Publication date
2018
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01A - Journal article
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IEEE Transactions on Signal Processing
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Volume
66
Issue / Number
14
Pages / Duration
3768-3783
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IEEE
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Abstract
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.
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1053-587X
1941-0476
1941-0476
Language
English
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No
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Published
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Closed
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Citation
Wildhaber, R., Zalmai, N., Jacomet, M., & Loeliger, H.-A. (2018). Windowed state-space filters for signal detection and separation. IEEE Transactions on Signal Processing, 66(14), 3768–3783. https://doi.org/10.1109/tsp.2018.2833804