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Proceedings Paper

The effects of compressive sensing on extracted features from tri-axial swallowing accelerometry signals
Author(s): Ervin Sejdić; Faezeh Movahedi; Zhenwei Zhang; Atsuko Kurosu; James L. Coyle
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Paper Abstract

Acquiring swallowing accelerometry signals using a comprehensive sensing scheme may be a desirable approach for monitoring swallowing safety for longer periods of time. However, it needs to be insured that signal characteristics can be recovered accurately from compressed samples. In this paper, we considered this issue by examining the effects of the number of acquired compressed samples on the calculated swallowing accelerometry signal features. We used tri-axial swallowing accelerometry signals acquired from seventeen stroke patients (106 swallows in total). From acquired signals, we extracted typically considered signal features from time, frequency and time-frequency domains. Next, we compared these features from the original signals (sampled using traditional sampling schemes) and compressively sampled signals. Our results have shown we can obtain accurate estimates of signal features even by using only a third of original samples.

Paper Details

Date Published: 4 May 2016
PDF: 5 pages
Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 985704 (4 May 2016); doi: 10.1117/12.2225466
Show Author Affiliations
Ervin Sejdić, Univ. of Pittsburgh (United States)
Faezeh Movahedi, Univ. of Pittsburgh (United States)
Zhenwei Zhang, Univ. of Pittsburgh (United States)
Atsuko Kurosu, Univ. of Pittsburgh (United States)
James L. Coyle, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 9857:
Compressive Sensing V: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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