Onset detection of pulse-shaped bioelectrical signals using linear state space models
dc.accessRights | Anonymous | * |
dc.contributor.author | Waldmann, Frédéric | |
dc.contributor.author | Baeriswyl, Christof | |
dc.contributor.author | Andonie, Raphael | |
dc.contributor.author | Wildhaber, Reto | |
dc.date.accessioned | 2023-02-16T13:19:08Z | |
dc.date.available | 2023-02-16T13:19:08Z | |
dc.date.issued | 2022-09-02 | |
dc.description.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. | en_US |
dc.identifier.doi | 10.1515/cdbme-2022-1027 | |
dc.identifier.issn | 2364-5504 | |
dc.identifier.uri | https://irf.fhnw.ch/handle/11654/34632 | |
dc.identifier.uri | https://doi.org/10.26041/fhnw-4645 | |
dc.issue | 2 | en_US |
dc.language.iso | en | en_US |
dc.publisher | De Gruyter | en_US |
dc.relation.ispartof | Current Directions in Biomedical Engineering | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.subject | Linear state space models | en_US |
dc.subject | Recursive least squares | en_US |
dc.subject | Likelihood | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Onset detection | en_US |
dc.subject | Ecg waves | en_US |
dc.subject.ddc | 600 - Technik, Medizin, angewandte Wissenschaften | en_US |
dc.title | Onset detection of pulse-shaped bioelectrical signals using linear state space models | en_US |
dc.type | 01A - Beitrag in wissenschaftlicher Zeitschrift | |
dc.volume | 8 | en_US |
dspace.entity.type | Publication | |
fhnw.InventedHere | Yes | en_US |
fhnw.IsStudentsWork | no | en_US |
fhnw.ReviewType | Anonymous ex ante peer review of a complete publication | en_US |
fhnw.affiliation.hochschule | Hochschule für Life Sciences FHNW | de_CH |
fhnw.affiliation.institut | Institut für Medizintechnik und Medizininformatik | de_CH |
fhnw.openAccessCategory | Gold | en_US |
fhnw.pagination | 101-104 | en_US |
fhnw.publicationState | Published | en_US |
relation.isAuthorOfPublication | 83c99bf4-07c1-4188-9d4c-09f921eee746 | |
relation.isAuthorOfPublication | 66894b38-407a-46f1-9e66-f573a64bf357 | |
relation.isAuthorOfPublication.latestForDiscovery | 83c99bf4-07c1-4188-9d4c-09f921eee746 |
Dateien
Originalbündel
1 - 1 von 1
- Name:
- 10.1515_cdbme-2022-1027.pdf
- Größe:
- 1.17 MB
- Format:
- Adobe Portable Document Format
- Beschreibung:
Lizenzbündel
1 - 1 von 1
Kein Vorschaubild vorhanden
- Name:
- license.txt
- Größe:
- 1.37 KB
- Format:
- Item-specific license agreed upon to submission
- Beschreibung: