Publications – Details
Deconvolution-based Physiological Signal Simplification for Periodical Parameter Estimation
- Authors:
- Stefan Liebich and Christoph Brüser
- Book Title:
- Proceedings of International Student Conference on Electrical Engineering (POSTER)
- Venue:
- Prague
- Event Date:
- 15.-15.5.2014
- Organization:
- Faculty of Electrical Engineering, CTU Prague
- Date:
- May 2014
- Note:
- 2. Best Poster Award
- Language:
- English
Abstract
The estimation of physiological parameters from raw signal recordings is absolutely crucial in modern clinical applications. A wide variety of these parameter incorporate a periodic nature, such as the heart or the respiration rate. Especially unobstructive, novel measurement techniques are characterized by complex waveforms, which are likely to change during the measurement. Simple peak detection algorithms are often not suited for these applications. One way to tackle these challenges is a preprocessing step for the simplification of the physiological signals. A novel deconvolution based approach for this preprocessing is introduced and evaluated in this paper. Two deconvolution methods are regarded, the Minimum Entropy Deconvolution (MED) and the Maximum Correlated Kurtosis Deconvolution (MCKD). Important parameters are outlined and examined. Finally the methods are validated using artificial as well as real clinical signals to demonstrate their potential.
Algorithms
Both deconvolution methods are available on MATLAB Central:
[G. L. McDonald, Q. Zhao, and M. Zuo, “Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection,” Mechanical Systems and Signal Processing, July 2012]
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