Onset detection of pulse-shaped bioelectrical signals using linear state space models
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Authors
Author (Corporation)
Publication date
02.09.2022
Typ of student thesis
Course of study
Type
01A - Journal article
Editors
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Parent work
Current Directions in Biomedical Engineering
Special issue
DOI of the original publication
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Series
Series number
Volume
8
Issue / Number
2
Pages / Duration
101-104
Patent number
Publisher / Publishing institution
De Gruyter
Place of publication / Event location
Edition
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Programming language
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Abstract
Bioelectrical signals are often pulse-shaped with superimposed interference signals. In this context, accurate identification of features such as pulse onsets, peaks, amplitudes, and duration is a frequent problem. In this paper, we present a versatile method of rather low computational complexity to robustly identify such features in real-world signals. For that, we take use of two straight-line models fit to the observations by minimizing a quadratic cost term, and then identify desired features by tweaked likelihood measures. To demonstrate the idea and facilitate access to the method, we provide examples from the field of cardiology.
Keywords
Linear state space models, Recursive least squares, Likelihood, Feature extraction, Onset detection, Ecg waves
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ISBN
ISSN
2364-5504
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Gold
License
Citation
Waldmann, F., Baeriswyl, C., Andonie, R., & Wildhaber, R. (2022). Onset detection of pulse-shaped bioelectrical signals using linear state space models. Current Directions in Biomedical Engineering, 8(2), 101–104. https://doi.org/10.1515/cdbme-2022-1027