Share This Article:

A Method for Automating Signal Analysis from Therapeutic Devices

Abstract Full-Text HTML XML Download Download as PDF (Size:2389KB) PP. 72-80
DOI: 10.4236/ica.2014.52008    3,447 Downloads   4,042 Views  

ABSTRACT

Many methods exist for cardiac and neural signal feature extraction and identification, but a published method for validation of therapeutic medical devices by computer analysis of their signals can be seldom found. This paper presents a simple, fast algorithm to extract the electrical stimulation including pulse width, exponential decay, and time between pulses from neurostimulators, pacemakers, implantable cardioverter defibrillators (ICDs), and transcutaneous electric nerve stimulators (TENS). An experimental validation demonstrated the automated analysis provide means to expedite device validation testing. In the future studies, the algorithm should be improved for its robustness and checked for analysis of signals with lower SNR. A figure of merit is provided to expedite electromagnetic compatibility (EMC) tests on the devices to ensure proper operation in the presence of electromagnetic emitters.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Eslami, M. and Guag, J. (2014) A Method for Automating Signal Analysis from Therapeutic Devices. Intelligent Control and Automation, 5, 72-80. doi: 10.4236/ica.2014.52008.

References

[1] Johannesen, L. and Galeotti, L. (2012) Automatic ECG Quality Scoring Methodology: Mimicking Human Annotators. Physiological Measurement, 33, 1479-1489.
http://dx.doi.org/10.1088/0967-3334/33/9/1479
[2] Paoletti, M. and Marchesi, C. (2006) Discovering Dangerous Patterns in Long-Term Ambulatory ECG Recordings Using a Fast QRS Detection Algorithm and Explorative Data Analysis. Computer Methods and Programs in Biomedicine, 82, 20-30.
http://dx.doi.org/10.1016/j.cmpb.2006.01.005
[3] Ricke, A.D., Swiryn, S., Bauernfeind, R.A., Conner, J.A., Young, B. and Rowlandson, G.I. (2011) Improved Pacemaker Pulse Detection: Clinical Evaluation of a New High-Bandwidth Electrocardiographic System. Journal of Electrocardiology, 2, 265-274.
http://dx.doi.org/10.1016/j.jelectrocard.2010.09.008
[4] Marchesi, C. and Paoletti, M. (2004) ECG Processing Algorithms for Portable Monitoring Units. Internet Journal of Medical Technology, 1, 23.
[5] Ricke, A. and Rowlandson, G.I. (2010) Module and Device for Discerning Therapeutic Signals from Noise in Physiological Data. US PTO: 20100317982.
[6] Ricke, A.D., Swiryn, S., Sahakian, A.V., Petrutiu, S., Young, B. and Rowlandson, G.I. (2008) The Relationship between Programmed Pacemaker Pulse Amplitude and the Surface Electrocardiogram Recorded Amplitude: Application of a New High-Bandwidth Electrocardiogram System. Journal of Electrocardiology, 41, 526-530.
http://dx.doi.org/10.1016/j.jelectrocard.2008.06.023
[7] Seidman, S.J., Brockman, R., Lewis, B.M., Guag, J, Shein, M.J., Clement, W.J., Kippola, J., Digby, D., Barber, C. and Huntwork, D. (2010) In Vitro Tests Reveal Sample Radiofrequency Identification Readers Inducing Clinically Significant Electromagnetic Interference to Implantable Pacemakers and Implantable Cardioverter-Defibrillators. Heart Rhythm, 1, 99-107.
http://dx.doi.org/10.1016/j.hrthm.2009.09.071
[8] Witters, D., Bassen, H., Guag, J., Addissie B., LaSorte, N. and Rafai, H. (2013) Assessment of Risks of Electromagnetic Interference for Personal Medical Electronic Devices (PMEDs) from Emissions of Millimeter Wave Security Screen Systems. Proceedings SPIE Defense, Security, and Sensing, Baltimore, 8711.

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.