Signal detection and discrimination for medical devices using windowed state space filters
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Publication date
2017
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04B - Conference paper
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Parent work
2017 13th IASTED International Conference on Biomedical Engineering (BioMed)
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Volume
Issue / Number
Pages / Duration
125-133
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Publisher / Publishing institution
IEEE
Place of publication / Event location
Innsbruck
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Abstract
We introduce a model-based approach for computationally efficient signal detection and discrimination, which is relevant for biological signals. Due to its low computational complexity and low memory need, this approach is well-suited for low power designs, as required for medical devices and implants. We use linear state space models to gain recursive, efficient computation rules and obtain the model parameters by minimizing the squared error on discrete-time observations. Furthermore we combine multiple models of different time-scales to match superpositions of signals of variable length. To give immediate access to our method, we highlight the use in several practical examples on standard and on esophageal ECG signals. This method was adapted and improved as part of a research and development project for medical devices.
Keywords
Subject (DDC)
600 - Technik, Medizin, angewandte Wissenschaften
Event
13th IASTED International Conference on Biomedical Engineering (BioMed)
Exhibition start date
Exhibition end date
Conference start date
20.02.2017
Conference end date
21.02.2017
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ISBN
978-0-88986-990-5
978-1-5090-4908-0
978-1-5090-4908-0
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Language
English
Created during FHNW affiliation
No
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Publication status
Published
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Peer review of the complete publication
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Closed
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Citation
WILDHABER, Reto, Nour ZALMAI, Marcel JACOMET und Hans-Andrea LOELIGER, 2017. Signal detection and discrimination for medical devices using windowed state space filters. In: 2017 13th IASTED International Conference on Biomedical Engineering (BioMed). Innsbruck: IEEE. 2017. S. 125–133. ISBN 978-0-88986-990-5. Verfügbar unter: https://irf.fhnw.ch/handle/11654/46840