The effects of compressive sensing on extracted features from tri-axial swallowing accelerometry signals.
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.
Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
Ervin
E
Sejdić
Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
Faezeh
F
Movahedi
Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
Zhenwei
Z
Zhang
Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA, 15260, USA.
Atsuko
A
Kurosu
Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA, 15260, USA.
James L
JL
Coyle
Compressive sensing; modulated discrete prolate spheroidal sequences; swallowing difficulties; tri-axial swallowing accelerometry signals
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