A radial basis classifier for the automatic detection of aspiration in children with dysphagia

Abstract

BackgroundSilent aspiration or the inhalation of foodstuffs without overt physiological signs presents a serious health issue for children with dysphagia. To date, there are no reliable means of detecting aspiration in the home or community. An assistive technology that performs in these environments could inform caregivers of adverse events and potentially reduce the morbidity and anxiety of the feeding experience for the child and caregiver, respectively. This paper proposes a classifier for automatic classification of aspiration and swallow vibration signals non-invasively recorded on the neck of children with dysphagia.MethodsVibration signals associated with safe swallows and aspirations, both identified via videofluoroscopy, were collected from over 100 children with neurologically-based dysphagia using a single-axis accelerometer. Five potentially discriminatory mathematical features were extracted from the accelerometry signals. All possible combinations of the five features were investigated in the design of radial basis function classifiers. Performance of different classifiers was compared and the best feature sets were identified.ResultsOptimal feature combinations for two, three and four features resulted in statistically comparable adjusted accuracies with a radial basis classifier. In particular, the feature pairing of dispersion ratio and normality achieved an adjusted accuracy of 79.8 ± 7.3%, a sensitivity of 79.4 ± 11.7% and specificity of 80.3 ± 12.8% for aspiration detection. Addition of a third feature, namely energy, increased adjusted accuracy to 81.3 ± 8.5% but the change was not statistically significant. A closer look at normality and dispersion ratio features suggest leptokurticity and the frequency and magnitude of atypical values as distinguishing characteristics between swallows and aspirations. The achieved accuracies are 30% higher than those reported for bedside cervical auscultation.ConclusionThe proposed aspiration classification algorithm provides promising accuracy for aspiration detection in children. The classifier is conducive to hardware implementation as a non-invasive, portable "aspirometer". Future research should focus on further enhancement of accuracy rates by considering other signal features, classifier methods, or an augmented variety of training samples. The present study is an important first step towards the eventual development of wearable intelligent intervention systems for the diagnosis and management of aspiration.

Baseline Characteristics of Dual-Axis Cervical Accelerometry Signals

“…4 It has been applied in several fields, 2,6 including the analysis of swallowing vibration signals. 7,19,20 The following steps can thus be taken to analyze the stationarity of a time series with the reverse arrangement test 4,7 :…”

“…39,47 An alternative approach for a noninvasive assessment of swallowing disorders is swallowing accelerometry, 41,40 a technique that involves an accelerometer placed on the neck to monitor vibrations associated with swallowing activities. Although single-axis accelerometers were traditionally used, 7,12,19,42 recent contributions showed that dual-axis accelerometers yield more information and enhance analysis capabilities, 20,46 likely because of the two-dimensional movement of the hyoid and the larynx during swallowing. 18,15 Even though accelerometry is becoming a valuable technique for the assessment of swallowing difficulties, some fundamental issues remain unaddressed.…”

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

“…These variables have been used in previous studies of dual-axis accelerometry signals by Lee et al [34] and represent fundamental summary statistics and spectral features of the signal. For the present study, signal characteristics were computed separately for each of the 500 swallows and then averaged for each participant, for each swallow task (saliva and water), and for each axis (AP and SI).…”

Swallow segmentation with artificial neural networks and multi-sensor fusion

“…Although a microphone can be used, the combination of an accelerometer and digital signal processing has been the recent focus of research and termed swallowing accelerometry [2,[13][14][15]. One accelerometry study has shown a significant correlation between peak neck vibration and maximum hyolaryngeal excursion [3], which is a vital biomechanical component of airway protection during swallowing [16].…”

“…pulse oximetry [1], neck vibrations [2,3], cervical auscultation [4], surface electromyography [5], or nasal airflow [6]). Such signal modalities are attractive due to their low cost, easy-to-attach sensors, and portability.…”