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Coordinate Mapping of Hyolaryngeal Mechanics in Swallowing
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Swallowing accelerometry signal feature variations with sensor displacement.

Khondaker A Mamun1, Catriona M Steele2, Tom Chau3

  • 1Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Bangladesh.

Medical Engineering & Physics
|May 25, 2015
PubMed
Summary

Dual-axis accelerometry shows promise for detecting swallowing impairment. Sensor placement variations up to 6mm superiorly and 4mm inferiorly/laterally do not significantly alter time-frequency features, ensuring classifier accuracy.

Keywords:
AccelerometryDysphagiaSwallowing

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Swallowing Physiology

Background:

  • Dual-axis accelerometry is a promising non-invasive technique for detecting swallowing impairment.
  • Accuracy of current signal processing classifiers for aspiration detection may be threatened by variations in sensor placement.

Purpose of the Study:

  • To investigate the impact of sensor placement variations on accelerometry signal characteristics for swallowing detection.
  • To determine the admissible region for sensor placement without compromising signal features.

Main Methods:

  • Recorded water swallows in 14 healthy adults using a dual-axis accelerometer.
  • Tested 13 sensor positions relative to a baseline below the thyroid cartilage.
  • Extracted signal features in time, frequency, and time-frequency domains.
  • Analyzed the effect of sensor position on feature distributions using non-parametric statistics.

Main Results:

  • Sensor displacement up to 4 mm inferiorly/laterally and 6 mm superiorly did not significantly alter time-frequency features.
  • The admissible region for sensor placement spans 10 mm (superior-inferior) and 8 mm (medial-lateral).
  • Time-frequency representations demonstrated the most robustness to sensor placement variations.

Conclusions:

  • Time-frequency features of accelerometry signals are robust to minor sensor placement variations.
  • Swallowing accelerometry classifiers utilizing time-frequency features can likely tolerate small sensor location deviations without performance degradation.