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

Abstract

Background: Silent 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.

Methods: Vibration 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.

Results: Optimal 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.

Conclusion: The 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.

Figure 1

Data collection set-up for the simultaneous acquisition of time-synchronized videofluoroscopic and accelerometric data.

Figure 2

Radial basis function architecture for aspiration detection, shown here with all five features. S = stationarity, N = normality, D = dispersion ratio, Z = zero-crossings, E = energy.

Figure 3

Sample swallow signals on the left and aspiration signals on the right. Note that swallows are typically longer in duration and dominated by low frequency components. Aspirations come in many flavours, some with noticeable high frequency elements (top and middle graphs on right side), but others with predominantly low frequency components (bottom right graph).

Figure 4

Notched boxplots showing change in adjusted accuracy as the number of features are increased from 1 to 5. Only the best feature combination for each number of features is shown.

Figure 5

Normality-dispersion ratio plane. With these 2 features, swallows and aspirations appear to be well separated. Note that one outlying observation was omitted from this plot for the sake of clarity.

Figure 6

Skewness and kurtosis of aspirations (top row) and swallows (bottom row).

Figure 7

Overview of the aspirometer: one possible implementation of the proposed classifier.