Analysis and Classification of Swallowing Sounds Using Reconstructed Phase Space Features

Abstract

The paper presents a quantitative analysis of swallowing sounds in normal and dysphagic subjects based on nonlinear dynamics metric tools. In addition, an automated method is proposed to identify patients at risk of dysphagia. Multidimensional phase space representation of the swallowing sound was reconstructed using Takens method of delays. Rosenstein and false nearest neighbor (FNN) methods were employed to evaluate the optimum time delay and proper embedding dimension, respectively. A Grassberger-Procaccia algorithm was utilized to calculate the correlation dimension as a measure of the complexity of the reconstructed attractor. The analysis demonstrated the low-dimensional dynamic characteristics of normal and dysphagic swallowing sounds. The optimum time delay and correlation dimension of the opening and transmission phases of swallowing sounds were used as features for a 3-nearest neighbor classifier to identify individuals at risk of dysphagia. The method was applied to tracheal sound recordings of 15 healthy subjects and 11 patients with some degree of dysphagia. The algorithm was able to classify 83% of swallows correctly. Finally, a screening algorithm was used which correctly classified 24 out of 26 subjects. This study suggests that nonlinear analysis is a promising tool for quantitative analysis of swallowing sounds and swallowing disorders.