Wavelet modeling paradigms for cardiovascular physiological signal interpretation
First Claim
Patent Images
1. A method of processing a cardiovascular physiological signal, comprising:
- decomposing the cardiovascular physiological signal into a first plurality of wavelet coefficients using a wavelet transform;
selecting a second plurality of wavelet coefficients from the first plurality of wavelet coefficients, the second plurality being a subset of the first plurality;
classifying or clustering the cardiovascular physiological signal into one of a plurality of predetermined classes based on the second plurality of wavelet coefficients using an artificial neural network; and
computing a regularized variance of each of the first plurality of wavelet coefficients, wherein the second plurality of wavelet coefficients are selected based on the regularized variance.
0 Assignments
0 Petitions
Accused Products
Abstract
Described herein is a method of processing a cardiovascular physiological signal, comprising: decomposing the cardiovascular physiological signal into a first plurality of wavelet coefficients using a wavelet transform; selecting a second plurality of wavelet coefficients from the first plurality of wavelet coefficients, the second plurality being a subset of the first plurality; classifying or clustering the cardiovascular physiological signal into one of a plurality of predetermined classes based on the second plurality of wavelet coefficients using an artificial neural network.
16 Citations
20 Claims
-
1. A method of processing a cardiovascular physiological signal, comprising:
-
decomposing the cardiovascular physiological signal into a first plurality of wavelet coefficients using a wavelet transform; selecting a second plurality of wavelet coefficients from the first plurality of wavelet coefficients, the second plurality being a subset of the first plurality; classifying or clustering the cardiovascular physiological signal into one of a plurality of predetermined classes based on the second plurality of wavelet coefficients using an artificial neural network; and computing a regularized variance of each of the first plurality of wavelet coefficients, wherein the second plurality of wavelet coefficients are selected based on the regularized variance. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 13)
-
-
10. A method of processing a cardiovascular physiological signal, comprising:
-
decomposing the cardiovascular physiological signal into a first plurality of wavelet coefficients using a wavelet transform; selecting a second plurality of wavelet coefficients from the first plurality of wavelet coefficients, the second plurality being a subset of the first plurality; classifying or clustering the cardiovascular physiological signal into one of a plurality of predetermined classes based on the second plurality of wavelet coefficients using an artificial neural network; and computing separation values of the first plurality of wavelet coefficients using a training set of cardiovascular physiological signals, wherein the second plurality of wavelet coefficients are selected based on the separation values of the first plurality of wavelet coefficients, wherein each of the cardiovascular physiological signals of the training set is known to belong to one of the predetermined classes and at least one of cardiovascular physiological signals of the training set belongs to each given one of the predetermined classes. - View Dependent Claims (11, 12, 17, 19)
-
-
14. A method of processing a cardiovascular physiological signal, comprising:
-
decomposing the cardiovascular physiological signal into a first plurality of wavelet coefficients using a wavelet transform; selecting a second plurality of wavelet coefficients from the first plurality of wavelet coefficients, the second plurality being a subset of the first plurality; classifying or clustering the cardiovascular physiological signal into one of a plurality of predetermined classes based on the second plurality of wavelet coefficients using an artificial neural network; and computing a coefficient of variation of each of the first plurality of wavelet coefficients, wherein the second plurality of wavelet coefficients are selected based on the coefficients of variation. - View Dependent Claims (15, 16, 18, 20)
-
Specification