Baseline Characteristics of Dual-Axis Cervical Accelerometry Signals

Abstract

Dual-axis swallowing accelerometry is a promising noninvasive tool for the assessment of difficulties during deglutition. The resting and anaerobic characteristics of these signals, however, are still unknown. This paper presents a study of baseline characteristics (stationarity, spectral features, and information content) of dual-axis cervical vibrations. In addition, modeling of a data acquisition system was performed to annul any undesired instrumentation effects. Two independent data collection procedures were conducted to fulfil the goals of the study. For baseline characterization, data were acquired from 50 healthy adult subjects. To model the data acquisition (DAQ) system, ten recordings were obtained while the system was exposed to random small vibrations. The inverse filtering approach removed extraneous effects introduced by the DAQ system. Approximately half of the filtered signals were stationary in nature. All signals exhibited a level of statistical dependence between the two axes. Also, there were very low frequency oscillations present in these signals that may be attributable to vasomotion of blood vessels near the thyroid cartilage, blood flow, and respiration. Demographic variables such as age and gender did not statistically influence baseline information-theoretic signal characteristics. However, participant age did affect the baseline spectral characteristics. These findings are important to the further development of diagnostic devices based on dual-axis swallowing vibration signals.

Compressive sampling of swallowing accelerometry signals using time-frequency dictionaries based on modulated discrete prolate spheroidal sequences

“…From a physiological stand point, the S-I axis appears to be as worthy of investigation as the A-P axis because the maximum excursion of the the hyolaryngeal structure during swallowing is of similar magnitude in both the anterior and superior directions [36,37]. Recent contributions have indeed confirmed that dual-axis accelerometers yield more information and enhance analysis capabilities [38][39][40][41][42][43].…”

Dysphagia Screening: Contributions of Cervical Auscultation Signals and Modern Signal-Processing Techniques

“…Many studies, in fact, utilize the raw transducer signal to draw their conclusions and many used the human ear in the analysis without any digital or mathematical analysis of the signal waveform’s characteristics [14]–[17], [19], [20], [22], [27], [32]–[37], [40], [48], [50], [52], [63], [76]–[79], [91]–[112]. Those studies that have conditioned the signal between acquisition and analysis generally only applied a bandpass filter in order to eliminate sources of noise at either end of the frequency spectrum [13], [28], [38], [39], [43]–[47], [49], [51], [53]–[62], [65], [68]–[73], [84]–[89]. Once again Takahashi, et al’s work [16], which was later supported by Youmans, et al [17], is cited often because their study characterized the frequency range of swallowing accelerometry signals.…”

“…Unfortunately, these studies have been unable to determine the lower bound on useful frequency components, resulting in much more variability of the placement of the lower notch. While some place it as low as 0.1 Hz in order to maintain a “pure” signal [21], [49], [51], [65], [66], [83], [86], [90], others place it as high as 30 Hz or more in order to eliminate motion artifacts and other low frequency noise [39], [42], [44], [60]–[62], [69]. Since similar bandlimits have yet to be identified for swallowing sounds, studies which use a microphone simply limit the recorded signal to either the human audible range [21], [32], [33], [37], [40], [46], [48], [67], [76]–[78], [86], [89], [95], [97]–[100], [102], [103], [110], [112]–[116] or the range of common stethoscopes used in bedside assessments [13], [22], [28], [39], [56], [69], [73], [88], [91], [92].…”

Reputation-Based Neural Network Combinations

“…Specifically, the problem is to differentiate between safe and unsafe swallowing on the basis of dual-axis accelerometry (Damouras et al, 2010;Sejdic, Komisar, Steele & Chau, 2010). The basic idea is to decompose a high dimensional classification problem into 3 lower dimensional problems, each with a unique subset of features and a dedicated classifier.…”

“…• The centroid frequency of the signal S (Sejdic, Komisar, Steele & Chau, 2010) can be estimated asf…”