Fast Wavelet Estimation of Weak Bio-signals Using Novel Algorithms for Generating Multiple Additional Data Frames
AssigneeEverest Biomedical Instruments Co.
InventorsElvir Causevic, Eldar Causevic, Mladen Wickerhauser
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Abstract
A method and apparatus for de-noising weak bio-signals having a relatively low signal to noise ratio utilizes an iterative process of wavelet de-noising a data set comprised of a new set of frames of wavelet coefficients partially generated through a cyclic shift algorithm. The method preferably operates on a data set having 2N frames, and the iteration is performed N−1 times. The resultant wavelet coefficients are then linearly averaged and an inverse discrete wavelet transform is performed to arrive at the de-noised original signal. The method is preferably carried out in a digital processor.
Claims14
A signal processor for de-noising an input signal, said signal processor including a memory for storing said input signal as an array A1 of a plurality of component frames, and a processor configured to:
- (a) perform a wavelet transform on each of said component frames to thereby create a corresponding array of wavelet coefficients;
- (b) iteratively for at least one repetition (i) combine multiple numbers of said wavelet coefficients into a plurality of resultant wavelet coefficients forming an array A2 of a like number 2N of wavelet coefficients, each of said resultant wavelet coefficients being formed from a different combination of wavelet coefficients determined from the original set of wavelet coefficients or the previous iteration, as appropriate;
- and (ii) wavelet de-noising each resultant wavelet coefficient of array A2;
- and
- (c) combine the resultant wavelet coefficients resulting from the last iteration to form a combined resultant wavelet coefficient.
A method for de-noising an input signal, said method utilizing a signal processor including a memory for storing said input signal as an array A1 of 2N component frames, said method comprising the steps of:
- (a) performing a discrete wavelet transform on each of said component frames to thereby determine a corresponding array of wavelet coefficients;
- (b) iteratively for N−1 repetitions (i) combining multiple numbers of the wavelet coefficients determined from the previous step or iteration, as appropriate, into a plurality of resultant wavelet coefficients forming an array A2 of a like number 2N of wavelet coefficients, each of said resultant wavelet coefficients being formed from a different combination of wavelet coefficients;
- and (ii) wavelet de-noising each wavelet coefficient of array A2;
- and
- (c) combining the resultant wavelet coefficients resulting from the iteration to form a combined resultant wavelet coefficient;
- and
- (d) performing an inverse discrete wavelet transform on the combined resultant wavelet coefficient to thereby produce the de-noised input signal.
Dependencies are inferred from the claim text.