Onset Detection of Pulse-Shaped Bioelectrical Signals Using Linear State Space Models
OPEN ACCESS
Author / Producer
Date
2022-08
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
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.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
8 (2)
Pages / Article No.
101 - 104
Publisher
De Gruyter
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
linear state space models; recursive least squares; likelihood; feature extraction; onset detection; ecg waves
Organisational unit
03568 - Loeliger, Hans-Andrea / Loeliger, Hans-Andrea
00002 - ETH Zürich