Deconvolution-based Physiological Signal Simplification for Periodical Parameter Estimation
The estimation of physiological parameters from raw sensor signals is absolutely crucial in modern clinical applications. A wide variety of these parameters incorporate a periodic nature, such as the heart rate or the respiration rate. This property can be exploited for their estimation. Particularly challenging is the processing of novel, unobtrusive measurement techniques, which are characterized by complex, time-varying waveforms. 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.
Keywords:
deconvolution, preprocessing, pitch estimation, periodical parameter estimation, interbeat interval estimation, ballistocardiogram
Autoři:
Stefan Liebich 1; Christoph Brüser 2; Steffen Leonhardt 2
Působiště autorů:
Institute of Communication Systems and Data Processing, RWTH Aachen University, Germany
1; Philips Chair for Medical Information Technology, RWTH Aachen University, Germany
2
Vyšlo v časopise:
Lékař a technika - Clinician and Technology No. 2, 2014, 44, 18-24
Kategorie:
Původní práce
Souhrn
The estimation of physiological parameters from raw sensor signals is absolutely crucial in modern clinical applications. A wide variety of these parameters incorporate a periodic nature, such as the heart rate or the respiration rate. This property can be exploited for their estimation. Particularly challenging is the processing of novel, unobtrusive measurement techniques, which are characterized by complex, time-varying waveforms. 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.
Keywords:
deconvolution, preprocessing, pitch estimation, periodical parameter estimation, interbeat interval estimation, ballistocardiogram
Zdroje
[1] Wiggins R. A. Minimum entropy deconvolution. Geoexploration, 1978, 16:21–35.
[2] McDonald G. L., Zhao Q., and Zuo M. J. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection. Mechanical Systems and Signal Processing, 2012, 33:237–255.
[3] Brüser C., Winter S., and Leonhardt S. Robust inter-beat interval estimation in cardiac vibration signals. Physiological Measurement, 2013, 34(2):123–38.
[4] Brüser C., Kortelainen J. M., Winter S., Tenhunen M., Pärkkä J., Leonhardt S. Multi-channel data fusion for heart rate estimation utilizing infrasonic cardiac signals. IEEE Journal of Biomedical and Health Informatics, (accepted for publication / in print), 2014.
[5] Hansen N. Artefaktbehandlung in pervasiv gemessenen kardiographischen Signalen (German). Master thesis, RWTH Aachen University, 2013.
[6] Kortelainen J. M., Gils M. V., and Pärkkä J. Multichannel Bed Pressure Sensor for Sleep Monitoring. Computing in Cardiology, 2012, pages 313–316.
[7] Nickerson W. A. Developments in minimum entropy deconvolution. Master thesis, University of British Columbia, 1986.
[8] Ooe M. and Ulrych T. J. Minimum Entropy Deconvolution with an Exponential Transformation. Geophysical Prospecting, 1979, 27:458–473.
Štítky
BiomedicínaČlánok vyšiel v časopise
Lékař a technika
2014 Číslo 2
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