Background Cervical auscultation with high resolution sensors is currently under consideration as a method of automatically screening for specific swallowing abnormalities. To be clinically useful without human involvement, any devices based on cervical auscultation should be able to detect specified swallowing events in an automatic manner. Methods In this paper, we comparatively analyze the density-based spatial clustering of applications with noise algorithm (DBSCAN), a k-means based algorithm, and an algorithm based on quadratic variation as methods of differentiating periods of swallowing activity from periods of time without swallows. These algorithms utilized swallowing vibration data exclusively and compared the results to a gold standard measure of swallowing duration. Data was collected from 23 subjects that were actively suffering from swallowing difficulties. Results Comparing the performance of the DBSCAN algorithm with a proven segmentation algorithm that utilizes k-means clustering demonstrated that the DBSCAN algorithm had a higher sensitivity and correctly segmented more swallows. Comparing its performance with a threshold-based algorithm that utilized the quadratic variation of the signal showed that the DBSCAN algorithm offered no direct increase in performance. However, it offered several other benefits including a faster run time and more consistent performance between patients. All algorithms showed noticeable differen-tiation from the endpoints provided by a videofluoroscopy examination as well as reduced sensitivity. Conclusions In summary, we showed that the DBSCAN algorithm is a viable method for detecting the occurrence of a swallowing event using cervical auscultation signals, but significant work must be done to improve its performance before it can be implemented in an unsupervised manner.

Accelerometry (the measurement of vibrations) and auscultation (the measurement of sounds) are both non-invasive techniques that have been explored for their potential to detect abnormalities in swallowing. The differences between these techniques and the information they capture about swallowing have not previously been explored in a direct comparison. In this study, we investigated the differences between dual-axis swallowing accelerometry and swallowing sounds by recording data from adult participants and calculating a number of time and frequency domain features. During the experiment, 55 participants (ages 18-65) were asked to complete five saliva swallows in a neutral head position. The resulting data was processed using previously designed techniques including wavelet denoising, spline filtering, and fuzzy means segmentation. The pre-processed signals were then used to calculate 9 time, frequency, and time-frequency domain features for each independent signal. Wilcoxon signed-rank and Wilcoxon rank-sum tests were utilized to compare feature values across transducers and patient demographics, respectively. In addition to finding a number of features that varied between male and female participants, our statistical analysis determined that the majority of our chosen features were statistically significantly different across the two sensor methods and that the dependence on within-subject factors varied with the transducer type. However, a regression analysis showed that age accounted for an insignificant amount of variation in our signals. We conclude that swallowing accelerometry and swallowing sounds provide different information about deglutition despite utilizing similar transduction methods. This contradicts past assumptions in the field and necessitates the development of separate analysis and processing techniques for swallowing sounds and vibrations.

Accelerometry (the measurement of vibrations) and auscultation (the measurement of sounds) are both non-invasive techniques that have been explored for their potential to detect abnormalities in swallowing. The differences between these techniques and the information they capture about swallowing have not previously been explored in a direct comparison. In this study, we investigated the differences between dual-axis swallowing accelerometry and swallowing sounds by recording data from adult participants and calculating a number of time and frequency domain features. During the experiment, 55 participants (ages 18-65) were asked to complete five saliva swallows in a neutral head position. The resulting data was processed using previously designed techniques including wavelet denoising, spline filtering, and fuzzy means segmentation. The pre-processed signals were then used to calculate 9 time, frequency, and time-frequency domain features for each independent signal. Wilcoxon signed-rank and Wilcoxon rank-sum tests were utilized to compare feature values across transducers and patient demographics, respectively. In addition to finding a number of features that varied between male and female participants, our statistical analysis determined that the majority of our chosen features were statistically significantly different across the two sensor methods and that the dependence on within-subject factors varied with the transducer type. However, a regression analysis showed that age accounted for an insignificant amount of variation in our signals. We conclude that swallowing accelerometry and swallowing sounds provide different information about deglutition despite utilizing similar transduction methods. This contradicts past assumptions in the field and necessitates the development of separate analysis and processing techniques for swallowing sounds and vibrations.

Electroencephalography (EEG) systems can enable us to study cerebral activation patterns during performance of swallowing tasks and possibly infer about the nature of abnormal neurological conditions causing swallowing difficulties. While it is well known that EEG signals are non-stationary, there are still open questions regarding the stationarity of EEG during swallowing activities and how the EEG stationarity is affected by different viscosities of the fluids that are swallowed by subjects during these swallowing activities. In the present study, we investigated the EEG signal collected during swallowing tasks by collecting data from 55 healthy adults (ages 18–65). Each task involved the deliberate swallowing of boluses of fluids of different viscosities. Using time-frequency tests with surrogates, we showed that the EEG during swallowing tasks could be considered non-stationary. Furthermore, the statistical tests and linear regression showed that the parameters of fluid viscosity, sex, and different brain regions significantly influenced the index of non-stationarity values. Therefore, these parameters should be considered in future investigations which use EEG during swallowing activities.

Background Decline in cognitive performance is associated with gait deterioration. Our objectives were: 1) to determine, from an original study in older community-dwellers without diagnosis of dementia, which gait parameters, among slower gait speed, higher stride time variability (STV) and Timed Up & Go test (TUG) delta time, were most strongly associated with lower performance in two cognitive domains (i.e., episodic memory and executive function); and 2) to quantitatively synthesize, with a systematic review and meta-analysis, the association between gait performance and cognitive decline (i.e., mild cognitive impairment (MCI) and dementia). Methods Based on a cross-sectional design, 934 older community-dwellers without dementia (mean±standard deviation, 70.3±4.9years; 52.1% female) were recruited. A score at 5 on the Short Mini-Mental State Examination defined low episodic memory performance. Low executive performance was defined by clock-drawing test errors. STV and gait speed were measured using GAITRite system. TUG delta time was calculated as the difference between the times needed to perform and to imagine the TUG. Then, a systematic Medline search was conducted in November 2013 using the Medical Subject Heading terms “Delirium,” “Dementia,” “Amnestic,” “Cognitive disorders” combined with “Gait” OR “Gait disorders, Neurologic” and “Variability.” Findings A total of 294 (31.5%) participants presented decline in cognitive performance. Higher STV, higher TUG delta time, and slower gait speed were associated with decline in episodic memory and executive performances (all P-values <0.001). The highest magnitude of association was found for higher STV (effect size  =  −0.74 [95% Confidence Interval (CI): −1.05;−0.43], among participants combining of decline in episodic memory and in executive performances). Meta-analysis underscored that higher STV represented a gait biomarker in patients with MCI (effect size  =  0.48 [95% CI: 0.30;0.65]) and dementia (effect size  = 1.06 [95% CI: 0.40;1.72]). Conclusion Higher STV appears to be a motor phenotype of cognitive decline.