We present a novel approach to estimating the mean square error (MSE) associated with any given threshold level in both hard and soft thresholding. The estimate is provided by using only the data that is being thresholded. This adaptive approach provides probabilistic confidence bounds on the MSE. The MSE bounds can be used to evaluate the denoising method. Our simulation results confirm that not only does the method provide an accurate estimate of the MSE for any given thresholding method, but the proposed method can also search and find an optimum threshold for any noisy data with regard to MSE.

Objective: The chin-down maneuver is commonly used in dysphagia management to facilitate greater airway protection. However, the literature suggests that variation in maneuver execution may threaten the effectiveness of the intervention. Our goal was to study variation in chin-down maneuver execution given a uniform instruction. Methods: Sagittal view digital video recordings were acquired from 408 healthy adults who performed sequences of reiterated water swallows in head-neutral and chin-down positions. Head angle measurements were extracted from the recordings, using markers on goggles worn by 176 participants. Results: We observed considerable variation in head angle in the head-neutral swallowing task, with a trend to greater flexion in participants over the age of 65. Male participants showed greater variation in head angle than females. Head flexion during the chin-down swallowing tasks averaged 19°, in the range reported to yield clinical benefit in radiographic studies. Conclusion: We conclude that a clear, uniform instruction is adequate to facilitate execution of the chin-down maneuver to a degree that is likely to be of clinical benefit. The variation in head angle observed in this study warrants further research, particularly regarding the relationship between anatomical cervical spine curvature and head angle influence on swallowing.

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.

Swallow accelerometry is an emerging tool for noninvasive dysphagia screening. However, the automatic detection of a swallowing event is challenging due to contaminant vibrations arising from head motion, speech and coughing. In this paper, we consider the acceleration signal as a stochastic diffusion where movement is associated with drift and swallowing with volatility. Using this model, we develop a volatility-based swallow event detector that operates on the raw acceleration signal in an online fashion. With data from healthy participants and patients with dysphagia, the proposed detector achieves performance comparable to previously proposed swallow segmentation algorithms, with the added benefit of online detection and no signal pre-processing. The volatility-based detector may be useful for event identification in other biomechanical applications that rely on accelerometry signals.