Characterizing Effortful Swallows from Healthy Community Dwelling Adults Across the Lifespan Using High-Resolution Cervical Auscultation Signals and MBSImP Scores: A Preliminary Study

Abstract

There is growing enthusiasm to develop inexpensive, non-invasive, and portable methods that accurately assess swallowing and provide biofeedback during dysphagia treatment. High-resolution cervical auscultation (HRCA), which uses acoustic and vibratory signals from non-invasive sensors attached to the anterior laryngeal framework during swallowing, is a novel method for quantifying swallowing physiology via advanced signal processing and machine learning techniques. HRCA has demonstrated potential as a dysphagia screening method and diagnostic adjunct to VFSSs by determining swallowing safety, annotating swallow kinematic events, and classifying swallows between healthy participants and patients with a high degree of accuracy. However, its feasibility as a non-invasive biofeedback system has not been explored. This study investigated 1. Whether HRCA can accurately differentiate between non-effortful and effortful swallows; 2. Whether differences exist in Modified Barium Swallow Impairment Profile (MBSImP) scores (#9, #11, #14) between non-effortful and effortful swallows. We hypothesized that HRCA would accurately classify non-effortful and effortful swallows and that differences in MBSImP scores would exist between the types of swallows. We analyzed 247 thin liquid 3 mL command swallows (71 effortful) to minimize variation from 36 healthy adults who underwent standardized VFSSs with concurrent HRCA. Results revealed differences ( p < 0.05) in 9 HRCA signal features between non-effortful and effortful swallows. Using HRCA signal features as input, decision trees classified swallows with 76% accuracy, 76% sensitivity, and 77% specificity. There were no differences in MBSImP component scores between non-effortful and effortful swallows. While preliminary in nature, this study demonstrates the feasibility/promise of HRCA as a biofeedback method for dysphagia treatment.

A review on intelligent aid diagnosis for dysphagia using swallowing sounds

“…Logistic Regression and Decision Tree yield the best performance with an accuracy of 99%, sensitivity of 100%, and specificity of 99%. However, the Decision Tree and Linear Discriminant Analysis algorithms, which demonstrated the best classification performance, achieved an average accuracy of only 76% for effortless and effortful swallowing 81 . These studies have shown that the accuracy of conventional machine learning algorithms used for binary classification of swallowing sounds (76%-99%) exceeds that of trained clinicians, whose accuracy is between 40% and 60% 4,89 .…”

“…Logistic Regression and Decision Tree yield the best performance with an accuracy of 99%, sensitivity of 100%, and specificity of 99%. However, the Decision Tree and Linear Discriminant Analysis algorithms, which demonstrated the best classification performance, achieved an average accuracy of only 76% for effortless and effortful swallowing 81 .…”

“…These studies have shown that the accuracy of conventional machine learning algorithms used for binary classification of swallowing sounds (76%-99%) exceeds that of trained clinicians, whose accuracy is between 40% and 60% 4,89 . However, Donohue et al 81,82 demonstrated a large discrepancy in the accuracy of conventional machine learning algorithms for different dichotic swallowing tasks. The reason for this discrepancy may be the selection of swallow sound signal features, as conventional machine learning algorithms rely on the quality of the selected signal features.…”

A Preliminary Investigation of Similarities of High Resolution Cervical Auscultation Signals Between Thin Liquid Barium and Water Swallows

“…This diagnostic barrier leaves the patient vulnerable to dysphagia-related complications [16] , [17] . Therefore, there is a high demand for a widely accessible dysphagia assessment utility that can perform accurate screening and provide insight regarding underlying swallowing physiology [18] – [21] .…”