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. 2010 Feb 4:9:7.
doi: 10.1186/1475-925X-9-7.

Effects of liquid stimuli on dual-axis swallowing accelerometry signals in a healthy population

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Effects of liquid stimuli on dual-axis swallowing accelerometry signals in a healthy population

Joon Lee et al. Biomed Eng Online. .

Abstract

Background: Dual-axis swallowing accelerometry has recently been proposed as a tool for non-invasive analysis of swallowing function. Although swallowing is known to be physiologically modifiable by the type of food or liquid (i.e., stimuli), the effects of stimuli on dual-axis accelerometry signals have never been thoroughly investigated. Thus, the objective of this study was to investigate stimulus effects on dual-axis accelerometry signal characteristics. Signals were acquired from 17 healthy participants while swallowing 4 different stimuli: water, nectar-thick and honey-thick apple juices, and a thin-liquid barium suspension. Two swallowing tasks were examined: discrete and sequential. A variety of features were extracted in the time and time-frequency domains after swallow segmentation and pre-processing. A separate Friedman test was conducted for each feature and for each swallowing task.

Results: Significant main stimulus effects were found on 6 out of 30 features for the discrete task and on 5 out of 30 features for the sequential task. Analysis of the features with significant stimulus effects suggested that the changes in the signals revealed slower and more pronounced swallowing patterns with increasing bolus viscosity.

Conclusions: We conclude that stimulus type does affect specific characteristics of dual-axis swallowing accelerometry signals, suggesting that associated clinical screening protocols may need to be stimulus specific.

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Figures

Figure 1
Figure 1
Accelerometry sensor placement. A sagittal view of the cervical region showing the orientation and polarity of the two axes of the accelerometer and nearby anatomical structures.
Figure 2
Figure 2
Axial energy contribution from wavelet decomposition. Relative energy contribution from each wavelet decomposition level in each axis, averaged over all swallows from both discrete and sequential tasks. Vertical and error bars represent means and standard deviations, respectively.

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