Subject health status (normal or disordered) was determined prior to the manometric experiment and accomplished using traditional assessments such as history and physical exam, modified barium swallow study, or fiberoptic endoscopic evaluation of swallowing. We achieved greater than 95% classification accuracy and agreement with health status determined using the aforementioned metrics. Therefore, different results were not obtained between traditional assessment tools and HRM with topical anesthetic. Also, though topical anesthesia was used in this study, it may not have significantly altered swallowing physiology with regard to our measurements (McCulloch et al., 2010
). Omitting topical anesthetic in pilot experiments led to increased gagging and resting UES pressure, confounding data collection. As swallowing is a sensorimotor phenomenon, impairing pharyngeal afferent nerves could potentially alter normal physiology. However, mechanoreceptors deep to the mucosa are largely responsible for modulating swallow physiology (Ali et al., 1997
) and these fibers were likely unaffected. Additionally, the oral mucosa was minimally affected, and afferent information from this area is also important to regulating swallow function. We believe that the benefit of increased subject comfort at the expense of short-term pain/temperature afferent alteration improved the reliability of our data.
Three classification model techniques were studied to determine effective discrimination between normal and disordered swallowing based on data extracted from HRM spatiotemporal plots. The ability to distinguish normal from disordered swallows is the first step in in distinguishing among different specific disorders, which is the goal of this type of analysis in a clinical setting. If normal subjects present with significant variation, then the likelihood of a classifier distinguishing among disorders is low. The three classification techniques used in this study were multi-layer perceptron (MLP), learning vector quantization (LVQ), and support vector machine (SVM). The multi-layer perceptron technique performed best, achieving an average classification accuracy of 96.44%. However, support vector machines classified normal versus disordered swallows with 85.39% accuracy, which is also considered a high success rate. These results suggest that these techniques, particularly the ANNs, can effectively distinguish normal from abnormal swallowing, which could be valuable clinically.
Our efforts to improve performance by modifying the architecture of the ANN, such as increasing the number of hidden nodes and codebook sizes, had a minimal effect in most cases. This is likely a consequence of implementing measures to prevent overfitting in large networks. Increasing the number of data points available by analyzing more swallows from a larger subject pool could potentially prevent this overfitting and allow these larger networks to run longer, potentially improving accuracy and generalization to new data (i.e. different types of dysphagia).
Differences between the three techniques could point to a lack of well defined clustering in the data or could be the result of combining dysphagic subjects into a single group rather than separating them by disorder. With both learning vector quantization and support vector machines, the winner-take-all nature of the learning algorithm means that correct classification depends to a great degree on the identification of clusters associated with particular output classes. The multilayer perceptron, though clearly improved by clustered data, is not as reliant on that condition since it lacks both the competitive nature of learning vector quantization and the direct partition construction, and inherent clustering, utilized by support vector machines.
Performing a feature reduction analysis allows us to determine which parameters are most frequently affected by dysphagia. Variations in these parameters may be sensitive indicators of swallowing abnormalities. Using maximum pre-opening UES pressure as the only parameter of interest, a classification accuracy of 79.32% was obtained. The accuracy obtained using this one parameter approached that using the entire feature set, demonstrating the impact of the UES to disruptions in swallowing physiology. Removing all UES-related parameters from the feature set resulted in the greatest decrease in classification accuracy (), in part due to the sensitivity of the maximum pre-opening UES pressure. As the UES was the region most sensitive to physiological abnormalities, we expected the UES integral to be a powerful parameter in distinguishing normal from disordered swallows; however, classification accuracy was only 55.40%. At our modest sample size in this preliminary stage, this may be due to some subjects exhibiting hypertonicity and some subjects exhibiting hypotonicity. Additionally, our method used to calculate the UES integral may have contributed to this as local pressure maximums occur far above resting UES pressure, but the integral we measured was the area above minimum UES pressure but below resting UES pressure. Extending the area of interest to include the area bounded by local pressure maximums, and thus integrating by parts over multiple sensor channels, may increase the utility of the parameter by more accurately accounting for the movement of the UES during swallowing. Interestingly, removing velopharyngeal pressure from the feature set did not greatly affect classification accuracy (), resulting in a decrease of only 1-2% depending on the classification method.
Average percent accurate classification for each classification model excluding a set of variables. Values are presented as mean ± standard deviation. L-M = Levenberg-Marquardt algorithm.
Based on the data presented in this study, UES abnormalities are likely the most common errant pressure feature associated with dysphagia, at least for our subject pool. As the UES requires fairly complex and appropriately timed sphincteric action, this is not surprising. Bolus gravitational force may be sufficient to compensate for a dysfunctional velopharynx or tongue base and elevated velopharyngeal pressure may adjust for low tongue base pressure. However, UES opening to facilitate bolus passage to the esophagus and closing to prevent regurgitation and reflux are critical aspects of a functional swallow.
Even at this preliminary stage, the pattern recognition techniques employed here appear to be clinically useful in distinguishing normal from abnormal swallowing. We recognize that the ultimate goal of a swallowing evaluation is to define the underlying physiologic abnormality that impairs successful swallow function. However, an immediate report on whether a subject’s swallow is normal or disordered could aid clinicians in patient screening and assessment based on pharyngeal HRM. The next step is to define manometric abnormalities according to dysphagia characteristics, which would be further aided by coupling HRM with videofluoroscopy. Although this study focused on differentiating normal and disordered swallows, the many features generated by our analysis of HRM data could prove able to distinguish between different types of dysphagia. The high accuracy in this preliminary study provides evidence that HRM has potential as an alternative clinical assessment tool, especially when coupled with ANN techniques.