Spelling suggestions: "subject:"dualaxis accelerometry"" "subject:"dualaxis acceletometry""
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Quantitative Classification of Pediatric Swallowing through AccelerometryMérey, Céleste 04 December 2012 (has links)
Swallowing accelerometry may provide a portable and cost-effective bedside alternative to currently available instrumentation. In this study, dual-axis accelerometry signals were collected simultaneous to videofluoroscopic records from 29 pediatric participants (age 6.8 $\pm$ 4.8 years; 20 males) previously diagnosed with neurogenic dysphagia. Videofluoroscopic records were reviewed by a clinical expert to extract swallow timings and ratings. The dual-axis accelerometry signals corresponding to each identified swallow were pre-processed, segmented and trimmed prior to feature extraction from time, frequency, time-frequency and information theoretic domains. Feature space dimensionality was reduced via principal components. Using 8-fold cross-validation, 16-18 dimensions and a support vector machine classifier with an RBF kernel, an adjusted accuracy of 89.6\% $\pm$ 0.9 was achieved for the discrimination between swallows with and without airway entry. Our results suggest that dual-axis accelerometry has merit in the non-invasive detection of unsafe swallows in children and deserves further consideration as a pediatric medical device.
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Quantitative Classification of Pediatric Swallowing through AccelerometryMérey, Céleste 04 December 2012 (has links)
Swallowing accelerometry may provide a portable and cost-effective bedside alternative to currently available instrumentation. In this study, dual-axis accelerometry signals were collected simultaneous to videofluoroscopic records from 29 pediatric participants (age 6.8 $\pm$ 4.8 years; 20 males) previously diagnosed with neurogenic dysphagia. Videofluoroscopic records were reviewed by a clinical expert to extract swallow timings and ratings. The dual-axis accelerometry signals corresponding to each identified swallow were pre-processed, segmented and trimmed prior to feature extraction from time, frequency, time-frequency and information theoretic domains. Feature space dimensionality was reduced via principal components. Using 8-fold cross-validation, 16-18 dimensions and a support vector machine classifier with an RBF kernel, an adjusted accuracy of 89.6\% $\pm$ 0.9 was achieved for the discrimination between swallows with and without airway entry. Our results suggest that dual-axis accelerometry has merit in the non-invasive detection of unsafe swallows in children and deserves further consideration as a pediatric medical device.
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