Dysphagia (swallowing impairment) is a common and serious component following stroke and frequently involves penetration and/or aspiration (the entry of foreign material into the airway). It has been reported that a modest reduction in dysphagia-related health problems would result in multi-million dollar savings in the health care system and would prevent thousands of aspiration-related deaths annually. Therefore, it is important to monitor swallowing safety and efficiency on a daily basis. There is widespread recognition of the need to identify impaired swallowing safety and efficiency as early as possible in individuals at risk. This has led to the development of many different swallow screening protocols, which rely heavily on observation of overt signs of swallowing difficulty during water swallowing tasks. Nevertheless, even the most rigorously-developed swallow screening protocols have limited sensitivity and specificity detecting impaired swallowing safety and efficiency, compared to radiographic swallowing examinations. In particular, overt clinical signs implying aspiration (such as coughing and throat clearing) are frequently absent or volume-dependent. Therefore, it would be desirable if a simple, non-invasive technology and protocol for monitoring swallowing safety and efficiency could be introduced into routine initial clinical screening programs. If such a method could achieve similar or superior aspiration detection accuracy to the current best-performing swallow screening protocols, it would facilitate equivalent or improved detection of swallowing risk, while overcoming the limitations of perceptual clinical judgments and considerable burdens with respect to staff training and compliance. Over the past 4 years, our research team has developed a non-invasive swallow screening instrument, the Aspirometer, which detects problems based on the processing and analysis of swallowing vibrations. In a recent proof-of-concept study, using concurrent videofluoroscopic validation, the Aspirometer signals demonstrated with 90% sensitivity and 77% specificity, based on a training set of 154 thin liquid swallow recordings. The current study aims to further enhance the accuracy of the Aspirometer by acquiring swallowing accelerometry signals from a larger sample of individuals undergoing concurrent videofluoroscopy. Additionally, we will collect swallowing acoustic information with a co-located microphone in order to enhance the accuracy of signal processing classification algorithms to detect problems in swallowing safety and efficiency. Lastly, we will explore the potential of the Aspirometer to detect swallowing problems beyond the context of thin liquid swallows by collecting swallows of nectar-thick and honey-thick liquids. This last step will enable us to determine the ability of the Aspirometer to determine the safety of oral intake with texture-modified diets while patients are waiting for comprehensive assessment. This study is a necessary step in translating the findings of our previous basic research regarding swallowing accelerometry into a clinically useful tool for monitoring of swallowing safety and efficiency in patients with suspected dysphagia.

Public Health Relevance

Dysphagia (swallowing difficulty) is a serious health concern following stroke, and frequently involves penetration-aspiration (entry of material into the airway), which contribute to dysphagia-related health problems (e.g., aspiration pneumonia, malnutrition and dehydration). In this study, we will test the ability of a new, noninvasive device (the Aspirometer), to assess swallowing safety and efficiency by analyzing swallowing vibrations and sounds collected from the neck during swallowing of thin, nectar-thick and honey-thick liquids. We will compare the accuracy of the Aspirometer's results to gold-standard instrumental examination of swallowing.

National Institute of Health (NIH)
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
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Special Emphasis Panel (ZRG1)
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Michel, Mary E
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University of Pittsburgh
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Biomed Engr/Col Engr/Engr Sta
United States
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Movahedi, Faezeh; Coyle, James L; Sejdic, Ervin (2018) Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks. IEEE J Biomed Health Inform 22:642-652
Dudik, Joshua M; Coyle, James L; El-Jaroudi, Amro et al. (2018) Deep Learning for Classification of Normal Swallows in Adults. Neurocomputing 285:1-9
Zhang, Zhenwei; Coyle, James L; Sejdi?, Ervin (2018) Automatic hyoid bone detection in fluoroscopic images using deep learning. Sci Rep 8:12310
Sejdi?, Ervin; Orovi?, Irena; Stankovi?, Srdjan (2018) Compressive sensing meets time-frequency: An overview of recent advances in time-frequency processing of sparse signals. Digit Signal Process 77:22-35
Sejdi?, Ervin; Orovi?, Irena; Stankovi?, Srdjan (2018) A software companion for compressively sensed time-frequency processing of sparse nonstationary signals. SoftwareX 8:9-10
Dudik, Joshua M; Kurosu, Atsuko; Coyle, James L et al. (2018) Dysphagia and its effects on swallowing sounds and vibrations in adults. Biomed Eng Online 17:69
Movahedi, Faezeh; Kurosu, Atsuko; Coyle, James L et al. (2017) Anatomical Directional Dissimilarities in Tri-axial Swallowing Accelerometry Signals. IEEE Trans Neural Syst Rehabil Eng 25:447-458
Jestrovi?, Iva; Coyle, James L; Sejdi?, Ervin (2017) A fast algorithm for vertex-frequency representations of signals on graphs. Signal Processing 131:483-491
Movahedi, Faezeh; Kurosu, Atsuko; Coyle, James L et al. (2017) A comparison between swallowing sounds and vibrations in patients with dysphagia. Comput Methods Programs Biomed 144:179-187
Jestrovi?, Iva; Coyle, James L; Sejdi?, Ervin (2017) Differences in brain networks during consecutive swallows detected using an optimized vertex-frequency algorithm. Neuroscience 344:113-123

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