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dc.contributor.advisor Varman, Peter J.
dc.contributor.advisor Massoud, Yehia
dc.creatorWilhelm, Keith
dc.date.accessioned 2014-09-30T20:08:14Z
dc.date.available 2014-09-30T20:08:14Z
dc.date.created 2012-12
dc.date.issued 2012-10-01
dc.date.submitted December 2012
dc.identifier.citation Wilhelm, Keith. "Classification Techniques for Undersampled Electromyography and Electrocardiography." (2012) Master’s Thesis, Rice University. https://hdl.handle.net/1911/77325.
dc.identifier.urihttps://hdl.handle.net/1911/77325
dc.description.abstract Electrophysiological signals including electrocardiography (ECG) and electromyography (EMG) are widely used in clinical environments for monitoring of patients and for diagnosis of conditions including cardiac and neuromuscular disease. Due to the wealth of information contained in these signals, many additional applications would be facilitated by full-time acquisition combined with automated analysis. Recent performance gains in portable computing devices and large scale computing platforms provide the necessary computational resources to process and store this data; however challenges at the sensor level have prevented monitoring systems from reaching the practicality and convenience necessary for widespread, continuous use. In this thesis, we examine the feasibility of applying techniques from the compressive sensing field to the acquisition and analysis of electrophysiological signals. These techniques allow signals to be acquired in compressed form, thereby providing a means to reduce power consumption of monitoring devices. We demonstrate the effects of several methods of compressive sampling and reconstruction on standard compression and reconstruction error metrics. Additionally, we investigate the effects of compressive sensing on the accuracy of automated signal analysis techniques for extracting useful information from ECG and EMG signals.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectElectromyography
Electrocardiography
Support vector machine
Compressive sensing
dc.title Classification Techniques for Undersampled Electromyography and Electrocardiography
dc.contributor.committeeMember Clark, John W., Jr.
dc.contributor.committeeMember Koushanfar, Farinaz
dc.date.updated 2014-09-30T20:08:15Z
dc.identifier.slug 123456789/ETD-2012-12-249
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Electrical and Computer Engineering
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Masters
thesis.degree.name Master of Science


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