dual - axis swallowing accelerometry is an emerging tool for the assessment of dysphagia . these signals however can be very noisy as a result of physiological and motion artifacts . a novel scheme for denoising those signals is proposed , 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 based de - noising . it also yields smaller reconstruction errors ratio ) than mndl , sure and donoho denoising methods . when applied to dual - axis swallowing accelerometry signals , the proposed scheme improves the snr values for dry , wet and wet chin tuck swallows . these results are important to the further development of medical devices based on dual - axis swallowing accelerometry signals .