Onset Detection of Pulse-Shaped Bioelectrical Signals Using Linear State Space Models

Open access
Datum
2022-08Typ
- Journal Article
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. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000572567Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
Current Directions in Biomedical EngineeringBand
Seiten / Artikelnummer
Verlag
De GruyterThema
linear state space models; recursive least squares; likelihood; feature extraction; onset detection; ecg wavesOrganisationseinheit
00002 - ETH Zürich00002 - ETH Zürich
00002 - ETH Zürich
03568 - Loeliger, Hans-Andrea / Loeliger, Hans-Andrea