PROCEDURE FOR DENOISING DUAL-AXIS SWALLOWING ACCELEROMETRY SIGNALS

• US 20120271872A1
• Filed: 06/20/2010
• Published: 10/25/2012
• Est. Priority Date: 06/21/2009
• Status: Active Grant

• Current Assignee: Holland Bloorview Kids Rehabilitation Hospital
• Original Assignee: Holland Bloorview Kids Rehabilitation Hospital
• Inventors: Chau, Tom, Sejdic, Ervin
• Granted Patent: US 8,992,446 B2
• Time in Patent Office: Days
• US Class Current: 708/309
• CPC Class Codes: A61B 5/4205 Evaluating swallowing
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04

Abstract

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. 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 (MNDL) based de-noising. It also yields smaller reconstruction errors (i.e., higher signal-to-noise (SNR) 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.

Claims

1. A method for denoising dual-axis swallowing accelerometry signals comprising the steps of:

a. Obtaining a first set of N discrete time observations;

b. Obtaining a first sample x(n) of said first set of observations, wherein said sample has a noiseless component f(n) and a noise component ε

(n);

c. For each of said noiseless components f(n) estimate a wavelet coefficient c_k;

d. Estimate a variance {circumflex over (σ

)}_ε

of the noise component ε

(n) from a median value of N/2 observations;

e. Determine a threshold value τ

for said wavelet coefficient c_k;

f. Based on the estimated value of {circumflex over (σ

)}_ε

and said threshold value of τ

calculate an upper bound for a reconstruction error r_e;

g. Apply said reconstruction error r_e to the threshold value of τ

to determine an optimal threshold value τ

_opt.

2. The method of claim 1 further comprising the step of eliminating values c_k falling below τ

_opt.

3. The method of claim 2 wherein the step of eliminating values of c_k comprising a thresholding process.

4. The method of claim 3 wherein said thresholding process is a soft process.