A variable-length genetic algorithm for clustering and classification
Pattern Recognition Letters - Special issue on genetic algorithms
Pattern classification with genetic algorithms
Pattern Recognition Letters - Special issue on genetic algorithms
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster analysis: a further approach based on density estimation
Computational Statistics & Data Analysis
Genetic Algorithms
Random Data: Analysis and Measurement Procedures
Random Data: Analysis and Measurement Procedures
Smoothed Bagging with Kernel Bandwidth Selectors
Neural Processing Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On the mean accuracy of statistical pattern recognizers
IEEE Transactions on Information Theory
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
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Background: Dysphagia assessment involves diagnosis of individual swallows in terms of the depth of airway invasion and degree of bolus clearance. The videofluoroscopic swallowing study is the current gold standard for dysphagia assessment but is time-consuming and costly. An ideal alternative would be an automated abnormal swallow detection methodology based on non-invasive signals. Objective: Building upon promising results from single-axis cervical accelerometry, the objective of this study was to investigate the combination of dual-axis accelerometry and nasal airflow for classification of healthy and abnormal swallows in a patient population with dysphagia. Methods: Signals were acquired from 24 adult patients with dysphagia (17.8+/-8.8 swallows per patient). The abnormality of each swallow was quantified using 4-point videofluoroscopic rating scales for its depth of airway invasion, bolus clearance from the valleculae, and bolus clearance from the pyriform sinuses. For each scale, we endeavored to automatically discriminate between the 2 extreme ratings, yielding 3 separate binary classification problems. Various time, frequency, and time-frequency domain features were extracted. A genetic algorithm was deployed for feature selection. Smoothed bootstrapping was utilized to balance the two classes and provide sufficient training data for a multidimensional feature space. Results: A Euclidean linear discriminant classifier resulted in a mean adjusted accuracy of 74.7% for the depth of airway invasion rating, whereas Mahalanobis linear discriminant classifiers yielded mean adjusted accuracies of 83.7% and 84.2% for bolus clearance from the valleculae and pyriform sinuses, respectively. The bolus clearance from the valleculae problem required the lowest feature space dimensionality. Wavelet features were found to be most discriminatory. Conclusions: This exploratory study confirms that dual-axis accelerometry and nasal airflow signals can be used to discriminate healthy and abnormal swallows from patients with dysphagia. The fact that features from all signal channels contributed discriminatory information suggests that multi-sensor fusion is promising in abnormal swallow detection.