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Anthropometric and Demographic Correlates of Dual-Axis Swallowing Accelerometry Signal Characteristics: A Canonical Correlation Analysis

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Abstract

Swallowing accelerometry has been proposed as a potential minimally invasive tool for collecting assessment information about swallowing. The first step toward using sounds and signals for dysphagia detection involves characterizing the healthy swallow. The purpose of this article is to explore systematic variations in swallowing accelerometry signals that can be attributed to demographic factors (such as participant gender and age) and anthropometric factors (such as weight and height). Data from 50 healthy participants (25 women and 25 men), ranging in age from 18 to 80 years and with approximately equal distribution across four age groups (18-35, 36-50, 51-65, 66 and older) were analyzed. Anthropometric and demographic variables of interest included participant age, gender, weight, height, body fat percent, neck circumference, and mandibular length. Dual-axis (superior-inferior and anterior-posterior) swallowing accelerometry signals were obtained for five saliva and five water swallows per participant. Several swallowing signal characteristics were derived for each swallowing task, including variance, amplitude distribution skewness, amplitude distribution kurtosis, signal memory, total signal energy, peak energy scale, and peak amplitude. Canonical correlation analysis was performed between the anthropometric/demographic variables and swallowing signal characteristics. No significant linear relationships were identified for saliva swallows or for superior-inferior axis accelerometry signals on water swallows. In the anterior-posterior axis, signal amplitude distribution kurtosis and signal memory were significantly correlated with age (r = 0.52, P = 0.047). These findings suggest that swallowing accelerometry signals may have task-specific associations with demographic (but not anthropometric) factors. Given the limited sample size, our results should be interpreted with caution and replication studies with larger sample sizes are warranted.

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Acknowledgments

The authors are grateful to the Ontario Science Centre Payload Science Program and to Dr. Ervin Sejdic, Erin Yeates, Anna Ammoury, Joon Lee, Kadeen Johns, Julie Chan, and Katherine Chow for assistance with data collection and analysis. This research was supported by funding from an Ontario Centres of Excellence Proof of Principle Grant, Panacis Medical, and the Toronto Rehabilitation Institute. The authors acknowledge the support of the Toronto Rehabilitation Institute which receives funding under the Provincial Rehabilitation Research Program from the Ministry of Health and Long-term Care in Ontario. The views expressed do not necessarily reflect those of the ministry.

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Authors and Affiliations

  1. Institute of Biomaterials &Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada

    Fady Hanna, Tom Chau & Catriona M. Steele

  2. Toronto Rehabilitation Institute, 550 University Avenue, #12030, Toronto, ON, M5G 2A2, Canada

    Fady Hanna, Sonja M. Molfenter, Rebecca E. Cliffe & Catriona M. Steele

  3. Department of Speech-Language Pathology, University of Toronto, Toronto, ON, M5G 1V7, Canada

    Rebecca E. Cliffe & Catriona M. Steele

  4. Bloorview Kids Rehab, Toronto, ON, M4G 1R8, Canada

    Tom Chau & Catriona M. Steele

  5. Graduate Department of Rehabilitation Sciences, University of Toronto, Toronto, ON, M5S 2Z9, Canada

    Tom Chau & Catriona M. Steele

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  1. Fady Hanna
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Correspondence to Catriona M. Steele.

Glossary

Daubechies Wavelet

A family of orthogonal wavelets often used to generate a time-frequency representation of physiologic and biomechanical signals.

Frequency at Peak Intensity

(used by Cichero and Murdoch [19]): The peak frequency at the point of the peak intensity (FPI), measured in Hz, was defined as the highest frequency at the point of highest intensity and was obtained directly from the sound spectrogram.

Frequency Range

(used by Cichero and Murdoch [19]): The highest frequency of an acoustic signal, in Hz, obtained directly from a sound spectrogram.

Kurtosis

A measure of the “peakedness” of the probability distribution of a variable (in the case of this article, of the accelerometry signal’s amplitude distribution).

Peak Frequency

The frequency of the “loudest sound,” in Hz.

Peak Intensity

(used by Cichero and Murdoch [19]): The point of highest displacement of an acoustic signal on an energy contour (5-ms frame length, no smoothing), recorded in decibels (dB).

Scale

Generally, the “frequency” dimension of a wavelet transform. In this article, scale is the level of the discrete wavelet transform of the signal bearing the highest fraction of the signal energy.

Signal Memory

The lag at which the autocorrelation of the signal decays to 1/e of its maximum.

Skewness

A measure of the asymmetry of the probability distribution of a variable.

Variance

A measure of statistical dispersion, averaging the squared distance of a variable’s possible values from the mean.

Wavelet Transform

A wavelet is a mathematical function used to divide a signal into different scale and time components. A wavelet transform generates a multiresolution time-frequency representation of a signal. Wavelet transforms have advantages over traditional Fourier transforms for representing functions that have discontinuities and sharp peaks, and for accurately deconstructing and reconstructing finite, nonperiodic, and/or nonstationary signals.

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Hanna, F., Molfenter, S.M., Cliffe, R.E. et al. Anthropometric and Demographic Correlates of Dual-Axis Swallowing Accelerometry Signal Characteristics: A Canonical Correlation Analysis. Dysphagia 25, 94–103 (2010). https://doi.org/10.1007/s00455-009-9229-9

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