Dual-axis Swallowing Accelerometry Signals Research Articles

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

Monitoring physiological functions such as swallowing often generates large volumes of samples to be stored and processed, which can introduce computational constraints especially if remote monitoring is desired. In this article, we propose a compressive sensing (CS) algorithm to alleviate some of these issues while acquiring dual-axis swallowing accelerometry signals. The proposed CS approach uses a time-frequency dictionary where the members are modulated discrete prolate spheroidal sequences (MDPSS). These waveforms are obtained by modulation and variation of discrete prolate spheroidal sequences (DPSS) in order to reflect the time-varying nature of swallowing acclerometry signals. While the modulated bases permit one to represent the signal behavior accurately, the matching pursuit algorithm is adopted to iteratively decompose the signals into an expansion of the dictionary bases. To test the accuracy of the proposed scheme, we carried out several numerical experiments with synthetic test signals and dual-axis swallowing accelerometry signals. In both cases, the proposed CS approach based on the MDPSS yields more accurate representations than the CS approach based on DPSS. Specifically, we show that dual-axis swallowing accelerometry signals can be accurately reconstructed even when the sampling rate is reduced to half of the Nyquist rate. The results clearly indicate that the MDPSS are suitable bases for swallowing accelerometry signals.

A Method for Removal of Low Frequency Components Associated with Head Movements from Dual-Axis Swallowing Accelerometry Signals

Head movements can greatly affect swallowing accelerometry signals. In this paper, we implement a spline-based approach to remove low frequency components associated with these motions. Our approach was tested using both synthetic and real data. Synthetic signals were used to perform a comparative analysis of the spline-based approach with other similar techniques. Real data, obtained data from 408 healthy participants during various swallowing tasks, was used to analyze the processing accuracy with and without the spline-based head motions removal scheme. Specifically, we analyzed the segmentation accuracy and the effects of the scheme on statistical properties of these signals, as measured by the scaling analysis. The results of the numerical analysis showed that the spline-based technique achieves a superior performance in comparison to other existing techniques. Additionally, when applied to real data, we improved the accuracy of the segmentation process by achieving a 27% drop in the number of false negatives and a 30% drop in the number of false positives. Furthermore, the anthropometric trends in the statistical properties of these signals remained unaltered as shown by the scaling analysis, but the strength of statistical persistence was significantly reduced. These results clearly indicate that any future medical devices based on swallowing accelerometry signals should remove head motions from these signals in order to increase segmentation accuracy.

Understanding the statistical persistence of dual-axis swallowing accelerometry signals

Swallowing accelerometry is a biomechanical approach for the assessment of difficulties during deglutition. However, the effects of various swallowing tasks and different anthropometric/demographic variables on the statistical behavior of these accelerometric signals are unknown. In particular, to understand the statistical persistence of these signals, we used detrended fluctuation analysis (DFA) to analyze accelerometric data collected from 408 healthy participants during dry, wet and wet chin tuck swallowing tasks. The results of DFA were then examined for potential influences of age, gender or body mass index. Several important conclusions were reached. First, the strongest persistence was observed for the wet chin tuck swallows. Second, the vibrations in the superior–inferior (S–I) direction generally have stronger temporal dependencies than those in the anterior–posterior (A–P) direction. Both of these phenomena can be attributed to the dominating influence of head movements on the amplitude of vibrations in the S–I direction. Third, gender, age and body mass index of the participants did not impact the observed persistence for dry and wet chin tuck swallows, while a gender effect was identified for wet swallows. In particular, male participants experienced more Brownian-like statistical dependencies in their swallowing signals. Future developments in the field should attempt to remove signal components associated with strong statistical persistence, as they tend to be associated with non-swallowing phenomena.

Vocalization removal for improved automatic segmentation of dual-axis swallowing accelerometry signals

Automatic segmentation of dual-axis swallowing accelerometry signals can be severely affected by strong vocalizations. In this paper, a method based on periodicity detection is proposed to detect and remove such vocalizations. Periodic signal components are detected using conventional speech processing techniques and information from both axes are combined to improve vocalization detection accuracy. Experiments with 408 healthy subjects performing dry, wet, and wet chin tuck swallows show that the proposed method attains an average 95.3% sensitivity and 96.3% specificity. When applied in conjunction with an automatic segmentation algorithm, it is observed that segmentation accuracy improves by approximately 55%. These results encourage further development of medical devices for the detection of swallowing difficulties.

Effects of liquid stimuli on dual-axis swallowing accelerometry signals in a healthy population

BackgroundDual-axis swallowing accelerometry has recently been proposed as a tool for non-invasive analysis of swallowing function. Although swallowing is known to be physiologically modifiable by the type of food or liquid (i.e., stimuli), the effects of stimuli on dual-axis accelerometry signals have never been thoroughly investigated. Thus, the objective of this study was to investigate stimulus effects on dual-axis accelerometry signal characteristics. Signals were acquired from 17 healthy participants while swallowing 4 different stimuli: water, nectar-thick and honey-thick apple juices, and a thin-liquid barium suspension. Two swallowing tasks were examined: discrete and sequential. A variety of features were extracted in the time and time-frequency domains after swallow segmentation and pre-processing. A separate Friedman test was conducted for each feature and for each swallowing task.ResultsSignificant main stimulus effects were found on 6 out of 30 features for the discrete task and on 5 out of 30 features for the sequential task. Analysis of the features with significant stimulus effects suggested that the changes in the signals revealed slower and more pronounced swallowing patterns with increasing bolus viscosity.ConclusionsWe conclude that stimulus type does affect specific characteristics of dual-axis swallowing accelerometry signals, suggesting that associated clinical screening protocols may need to be stimulus specific.

A procedure for denoising dual-axis swallowing accelerometry signals

Dual-axis swallowing accelerometry is an emerging tool for the assessment of dysphagia (swallowing difficulties). These signals however can be very noisy as a result of physiological and motion artifacts. In this note, we propose a novel scheme for denoising those signals, i.e. a computationally efficient search for the optimal denoising threshold within a reduced wavelet subspace. To determine a viable subspace, the algorithm relies on the minimum value of the estimated upper bound for the reconstruction error. A numerical analysis of the proposed scheme using synthetic test signals demonstrated that the proposed scheme is computationally more efficient than minimum noiseless description length (MNDL)-based denoising. It also yields smaller reconstruction errors than MNDL, SURE and Donoho denoising methods. When applied to dual-axis swallowing accelerometry signals, the proposed scheme exhibits improved performance for dry, wet and wet chin tuck swallows. These results are important for the further development of medical devices based on dual-axis swallowing accelerometry signals.

Segmentation of Dual-Axis Swallowing Accelerometry Signals in Healthy Subjects With Analysis of Anthropometric Effects on Duration of Swallowing Activities

Dysphagia (swallowing difficulty) is a serious and debilitating condition that often accompanies stroke, acquired brain injury, and neurodegenerative illnesses. Individuals with dysphagia are prone to aspiration (the entry of foreign material into the airway), which directly increases the risk of serious respiratory consequences such as pneumonia. Swallowing accelerometry is a promising noninvasive tool for the detection of aspiration and the evaluation of swallowing. In this paper, dual-axis accelerometry was implemented since the motion of the hyolaryngeal complex occurs in both anterior-posterior and superior-inferior directions during swallowing. Dual-axis cervical accelerometry signals were acquired from 408 healthy subjects during dry, wet, and wet chin tuck swallowing tasks. The proposed segmentation algorithm is based on the idea of sequential fuzzy partitioning of the signal and is well suited for long signals with nonstationary variance. The algorithm was validated with simulated signals with known swallowing locations and a subset of 295 real swallows manually segmented by an experienced speech language pathologist. In both cases, the algorithm extracted individual swallows with over 90% accuracy. The time duration analysis was carried out with respect to gender, body mass index (BMI), and age. Demographic and anthropometric variables influenced the duration of these segmented signals. Male participants exhibited longer swallows than female participants (p=0.05). Older participants and participants with higher BMIs exhibited swallows with significantly longer (p=0.05) duration than younger participants and those with lower BMIs, respectively.

Time and time–frequency characterization of dual-axis swallowing accelerometry signals

Single-axis swallowing accelerometry has shown potential as a non-invasive clinical swallowing assessment tool. Previous swallowing accelerometry research has focused exclusively on the anterior–posterior vibration detected on the surface of the neck. However, hyolaryngeal motion during pharyngeal swallowing occurs in both the anterior–posterior and superior–inferior directions, suggesting that dual-axis accelerometry may be worthy of investigation. With this motivation, the present paper provides a characterization of dual-axis swallowing accelerometry signals from healthy adults in the time and time–frequency domains. Time-domain analysis revealed that signals in the two axes exhibited different probability density functions, and minimal cross-correlation and mutual information. Time–frequency analysis highlighted inter-axis dissimilarities in the scalograms, pseudo-spectra and temporal evolution of low- and high-frequency content. Therefore, it was concluded that the two axes contain different information about swallowing and that the superior–inferior axis should be further investigated in future swallowing accelerometry studies.