Automatic discrimination between safe and unsafe swallowing using a reputation-based classifier

Mohammad S Nikjoo,Catriona M Steele,Tom Chau,Ervin Sejdić

doi:10.1186/1475-925x-10-100

Abstract

BackgroundSwallowing accelerometry has been suggested as a potential non-invasive tool for bedside dysphagia screening. Various vibratory signal features and complementary measurement modalities have been put forth in the literature for the potential discrimination between safe and unsafe swallowing. To date, automatic classification of swallowing accelerometry has exclusively involved a single-axis of vibration although a second axis is known to contain additional information about the nature of the swallow. Furthermore, the only published attempt at automatic classification in adult patients has been based on a small sample of swallowing vibrations.MethodsIn this paper, a large corpus of dual-axis accelerometric signals were collected from 30 older adults (aged 65.47 ± 13.4 years, 15 male) referred to videofluoroscopic examination on the suspicion of dysphagia. We invoked a reputation-based classifier combination to automatically categorize the dual-axis accelerometric signals into safe and unsafe swallows, as labeled via videofluoroscopic review. From these participants, a total of 224 swallowing samples were obtained, 164 of which were labeled as unsafe swallows (swallows where the bolus entered the airway) and 60 as safe swallows. Three separate support vector machine (SVM) classifiers and eight different features were selected for classification.ResultsWith selected time, frequency and information theoretic features, the reputation-based algorithm distinguished between safe and unsafe swallowing with promising accuracy (80.48 ± 5.0%), high sensitivity (97.1 ± 2%) and modest specificity (64 ± 8.8%). Interpretation of the most discriminatory features revealed that in general, unsafe swallows had lower mean vibration amplitude and faster autocorrelation decay, suggestive of decreased hyoid excursion and compromised coordination, respectively. Further, owing to its performance-based weighting of component classifiers, the static reputation-based algorithm outperformed the democratic majority voting algorithm on this clinical data set.ConclusionGiven its computational efficiency and high sensitivity, reputation-based classification of dual-axis accelerometry ought to be considered in future developments of a point-of-care swallow assessment where clinical informatics are desired.

Highlights

Dysphagia refers to any swallowing disorder [1] and may arise secondary to stroke, multiple sclerosis, and eosinophilic esophagitis, among many other conditions [2]
Dysphagia may lead to aspiration pneumonia in which food and liquid enter the airway and into lungs [3]
Swallowing accelerometry has been proposed as a potential adjunct to video-fluoroscopic swallowing study (VFSS)

Summary

Introduction

Dysphagia refers to any swallowing disorder [1] and may arise secondary to stroke, multiple sclerosis, and eosinophilic esophagitis, among many other conditions [2]. The health of a swallow is judged by clinical experts according to criteria such as the depth of airway invasion and the degree of bolus clearance after the swallow This technique requires expensive and specialized equipment, ionizing radiation and significant human resources, thereby precluding its use in the daily monitoring of dysphagia [5]. In swallowing accelerometry, vocalizations are explicitly removed by preprocessing [7] and studies have implicated hyolaryngeal motion as the primary source of the acceleration signal [8,9]. Both the method of transduction and the primary physiological source of these signals are different. The only published attempt at automatic classification in adult patients has been based on a small sample of swallowing vibrations

Methods

Results

Discussion

Conclusion