In this study, the feasibility of acoustical analysis for detection of swallowing silent aspiration is investigated. As a pilot study, we analyzed the breath sounds of 21 dysphagic individuals, 11 of which demonstrated aspiration during the fiberoptic endoscopic evaluation of swallowing (FEES) or videofluoroscopic swallowing study (VFSS). We found that the low frequency components of the power spectrum of the breath sounds after a swallow show higher magnitude when there is aspiration. Thus, we divided the frequency range below 300 Hz into three sub-bands and calculated the average power of the breath sound signal in each sub-band as the characteristic features for the stage 1 classification into two groups of aspirated and non-aspirated patients. Then, for the aspirated group, the unsupervised fuzzy k-means clustering algorithm was deployed to label the breath sounds immediately after a swallow as aspiration or non-aspiration. The results were compared with the FEES/VFSS assessments provided by the speech language pathologists. The results are encouraging: more than 86 % accuracy in detection of silent aspiration. While the proposed method should be verified on a larger dataset, the results are promising for the use of acoustical analysis as a clinical tool to detect silent aspiration.

Swallowing aspiration is known as the most clinically significant symptom of swallowing disorders (dysphagia). Noninvasive methods for detection of aspiration (the entry of food into airway due to dysphagia) are of great interest as they will lead to better management of dysphagia; thus, the risk of pneumonia, length of hospital stay and overall health care expenses can be reduced. The risk of aspiration is much higher in severely dysphagic patients. Normally, aspiration is detected by an imaging technique during swallowing, which is time consuming, costly and requires the patient's cooperation. In this study, we investigated the application of acoustical analysis of breathing and swallowing sounds for identifying patients at high risk of aspiration. We propose a novel method based on phase-space analysis of breathing sounds immediately after the swallow followed by support vector machine classification for use as a diagnostic aid for identifying patients with high risk of aspiration. We evaluated the method using breath and swallowing sounds recorded from 50 dysphagic individuals, 27 of which demonstrated silent aspiration (without cough or throat clearance) during either fiberoptic endoscopic evaluation of swallowing (FEES) or videofluoroscopic swallowing (VFS) assessment. The classification result of the proposed method was compared with those of the FEES/VFS assessment provided by speech-language pathologists; it showed 91 % sensitivity and 85 % specificity in detection of patients with severe aspiration (high risk dysphagia). The result is promising to suggest the proposed phase-space acoustical analysis method as a quick and noninvasive screening clinical tool to detect patients developing severe aspiration.

Detecting aspiration after swallows (the entry of bolus into trachea) is often a difficult task particularly when the patient does not cough; those are called silent aspiration. In this study, the application of acoustical analysis in detecting silent aspiration is investigated. We recorded the swallowing and the breath sounds of 10 individuals with swallowing disorders, who demonstrated silent aspiration during the fiberoptic endoscopic evaluation of swallowing (FEES) assessment. We analyzed the power spectral density (PSD) of the breath sound signals following each swallow; the PSD showed higher magnitude at low frequencies for the breath sounds following an aspiration. Therefore, we divided the frequency range below 300 Hz into 3 sub-bands, over which we calculated the average power as the characteristic features for the classification purpose. Then, the fuzzy k-means unsupervised classification method was deployed to find the two clusters in the data set: the aspirated and non-aspirated groups. The results were evaluated using the FEES assessments provided by the speech language pathologists. The results show 82.3% accuracy in detecting swallows with silent aspiration. Although the proposed method should be verified on a larger dataset, the results are promising for the use of acoustical analysis as a clinical tool to detect silent aspiration.

In this paper, a mathematical modeling of the swallowing sound generation is presented. To evaluate the model, its application on swallowing disorder (dysphagia) diagnosis is discussed. As a starting point, a simple linear time invariant model is assumed to represent the pharyngeal wall and tissue excited by a train of impulses. The modeling is approached by two different assumptions. In one approach, it is assumed that the impulse train, representing the neural activities to trigger swallow, is the same for both groups of control and dysphagic, and it is the pharyngeal model that accounts for the difference between the two groups. On the other hand, in the second approach, it is assumed that the pharyngeal response is the same for both groups, but the neural activities to initiate the swallow are different between the two groups. The results show that the second approach complies better with the physiological characteristics of swallowing mechanism as it provides a much better discrimination between the swallowing sounds of control and dysphagic groups of this study. Though, it should be noted that our dysphagic group subjects were cerebral palsy and stroke patients. Hence, the model accounting for initiation of neural activities is reasonable to show better results.

In this paper the statistical properties of the swallowing sound is discussed. This knowledge is required for the acoustical modeling of the swallowing mechanism as it is important to select an appropriate type of the system (i.e. linear vs. nonlinear) for modeling. The tests of linearity and gaussianity were performed. The results of the statistical test of gaussianity showed a nonGaussian distribution of the swallowing sound signals. Also, the test of linearity exhibited the nonlinear characteristics of the model that represents the swallowing sound generation.