METHOD AND DEVICE FOR FILTERING, SEGMENTING, COMPRESSING AND CLASSIFYING OSCILLATORY SIGNALS

• US 20070260151A1
• Filed: 09/06/2006
• Published: 11/08/2007
• Est. Priority Date: 05/03/2006
• Status: Abandoned Application

• Current Assignee: Gari D. Clifford
• Original Assignee: Gari D. Clifford
• Inventors: Clifford, Gari D.
• Application Number:
  US11/470,506
• Publication Number:
  US 20070260151A1
• Time in Patent Office: Days
• US Class Current: 600/509
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method, system, and computer readable medium executable on a computer for at least one of filtering, segmenting, compressing and classifying an ECG or similar signal includes the steps of fitting a nonlinear signal model to the signal using an optimization algorithm, such as nonlinear least squares, and determining features in the nonlinear signal model.

Claims

1. A computer-implemented method for at least one of filtering, segmenting, compressing, and classifying an ECG signal comprising the steps of:

obtaining an ECG signal;

storing the ECG signal;

generating a nonlinear signal model based on the ECG signal;

fitting the nonlinear signal model to the ECG signal based on an optimization algorithm;

determining at least one feature of the ECG with the nonlinear signal model; and

outputting the at least one feature of the ECG based on the nonlinear signal model.

2. The method according to claim 1 wherein the step of generating a nonlinear signal model corresponds to modeling at least one segment of interest of the ECG signal selected from the group consisting of a QT interval, a Q-wave onset (PQ-junction), a T-wave offset, a T-wave height, a U-wave detection and characterization, and a T-wave asymmetry of the ECG signal.

3. The method according to claim 1 wherein said generating step comprises the use of Gaussian descriptors.

4. The method according to claim 1 wherein the optimization algorithm in said fitting step comprises least squares optimization.

5. The method according to claim 1 wherein said generating step comprises the use of Gaussian descriptors and the optimization algorithm comprises least squares optimization.

6. The method according to claim 1 wherein said step of determining at least one feature comprises determining at least one of a QT interval, a Q-wave onset (PQ-junction), a T-wave offset, a T-wave height, a U-wave detection and characterization, and a T-wave asymmetry of the ECG signal.

7. The method of claim 6 wherein said step of determining at least one feature comprises determining each one of a QT interval, a Q-wave onset (PQ-junction), a T-wave offset, a T-wave height, a U-wave detection and characterization, and a T-wave asymmetry of the ECG signal.

8. The method according to claim 1 wherein said step of determining at least one feature comprises determining the locations of the P, Q, R, S and T features of each beat of the ECG signal.

9. The method according to claim 1 wherein said generating step further comprises the steps of:

locating at least one fiducial point in the ECG signal;

performing a temporal average of time series segments around the at least one fiducial point;

accepting features inside a threshold; and

determining the symmetry of the features that are accepted.

10. The method according to claim 9 wherein said generating step further comprises the steps of:

fitting the model to the features; and

rejecting model fit when the model exceeds a threshold.

11. An adaptive filter using the method of claim 1.

12. The filter of claim 11, wherein said filter operates on a beat-by-beat basis.

13. A computer readable medium executable on a computer for at least one of filtering, segmenting, compressing, and classifying an oscillatory physiological signal, the computer readable medium executing the steps of:

obtaining an ECG signal;

storing the ECG signal;

generating a nonlinear signal model based on the ECG signal;

fitting the nonlinear signal model to the ECG signal based on an optimization algorithm;

determining at least one feature of the ECG with the nonlinear signal model; and

outputting the at least one feature of the ECG based on the nonlinear signal model.

14. The computer readable medium according to claim 13 wherein the step of generating a nonlinear signal model corresponds to modeling at least one segment of interest of the ECG signal selected from the group consisting of a QT interval, a Q-wave onset (PQ-junction), a T-wave offset, a T-wave height, a U-wave detection and characterization, and a T-wave asymmetry of the ECG signal.

15. The computer readable medium according to claim 13 wherein said step of generating comprises the use of Gaussian descriptors.

16. The computer readable medium according to claim 13 wherein the optimization algorithm in said fitting step comprises least squares optimization.

17. The computer readable medium according to claim 13 wherein said generating step comprises the use of Gaussian descriptors and the optimization algorithm comprises least squares optimization.

18. The computer readable medium according to claim 13 wherein said step of determining at least one feature comprises determining at least one of a QT interval, a Q-wave onset (PQ-junction), a T-wave offset, a T-wave height, a U-wave detection and characterization, and a T-wave asymmetry of the ECG signal.

19. The method of claim 18 wherein said step of determining at least one feature comprises determining each one of a QT interval, a Q-wave onset (PQ-junction), a T-wave offset, a T-wave height, a U-wave detection and characterization, and a T-wave asymmetry of the ECG signal.

20. The computer readable medium according to claim 13 wherein said step of determining at least one feature comprises determining the locations of the P, Q, R, S and T features of each beat of the ECG signal.

21. The computer readable medium according to claim 13 wherein said generating step further comprises the steps of:

locating at least one fiducial point in the ECG signal;

performing a temporal average of time series segments around the at least one fiducial point;

accepting features inside a threshold; and

determining the symmetry of the features that are accepted.

22. The computer readable medium according to claim 21 wherein said generating step further comprises the steps of:

fitting the model to the features; and

rejecting model fit when the model exceeds a threshold.

23. A computer system for at least one of filtering, segmenting, compressing and classifying an oscillatory physiological signal, the computer system comprising:

an input to receive an ECG signal;

a storage device responsive to the input to store the ECG signal;

a processor to generate a nonlinear signal model based on the ECG signal, fit the nonlinear signal model to the ECG signal based on an optimization algorithm, and determine at least one feature of the ECG with the nonlinear signal model; and

an output device to output the at least one feature of the ECG based on the nonlinear signal model.

24. The computer system according to claim 23 wherein the nonlinear signal model corresponds to at least one segment of interest of the ECG signal.

25. The computer system according to claim 23 wherein the nonlinear signal model comprises Gaussian descriptors.

26. The computer system according to claim 23 wherein the optimization algorithm comprises least squares optimization.

27. The computer system according to claim 23 wherein the nonlinear signal model comprises Gaussian descriptors and the optimization algorithm comprises least squares optimization.

28. The computer system according to claim 23 wherein the at least one feature comprises at least one of a QT interval, a Q-wave onset (PQ-junction), a T-wave offset, a T-wave height, a U-wave detection and characterization, and a T-wave asymmetry of the ECG signal.

29. The computer system according to claim 23 wherein the at least one feature comprises at least one of the locations of the P, Q, R, S and T features of each beat of the ECG signal.

30. A computer system for at least one of filtering, segmenting, compressing and classifying an oscillatory physiological signal, the computer system comprising:

means for receiving an ECG signal;

means for storing the ECG signal;

means for generating a nonlinear signal model based on the ECG signal, fitting the nonlinear signal model to the ECG signal based on an optimization algorithm, and determining at least one feature of the ECG with the nonlinear signal model; and

means for outputting the at least one feature of the ECG based on the nonlinear signal model.

31. The computer system according to claim 30 wherein the nonlinear signal model corresponds to at least one segment of interest of the ECG signal.

32. The computer system according to claim 30 wherein the nonlinear signal model comprises Gaussian descriptors.

33. The computer system according to claim 30 wherein the optimization algorithm comprises least squares optimization.

34. The computer system according to claim 30 wherein the nonlinear signal model comprises Gaussian descriptors and the optimization algorithm comprises least squares optimization.

35. The computer system according to claim 30 wherein the at least one feature comprises at least one of a QT interval, a Q-wave onset (PQ-junction), a T-wave offset, a T-wave height, a U-wave detection and characterization, and a T-wave asymmetry of the ECG signal.

36. The computer system according to claim 30 wherein the at least one feature comprises at least one of the locations of the P, Q, R, S and T features of each beat of the ECG signal.