Device for analysis of a signal, in particular a physiological signal such as an ECG signal

• US 7,359,749 B2
• Filed: 11/13/2003
• Issued: 04/15/2008
• Est. Priority Date: 11/14/2002
• Status: Expired due to Fees

• Current Assignee: Ela Medical S.A. (LivaNova Plc)
• Original Assignee: Ela Medical SAS (MicroPort Scientific Corp.)
• Inventors: Quenet, Brigette, Faisandier, Yves, Dubois, M. Remi
• Primary Examiner(s): Manuel; George
• Assistant Examiner(s): Flory; Christopher A.
• Application Number:
  US10/712,976
• Publication Number:
  US 20040162495A1
• Time in Patent Office: 1,615 Days
• Field of Search: 600/509
• US Class Current: 600/509
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/353 Detecting P-waves
• CPC Class Codes: A61B 5/355 Detecting T-waves
• CPC Class Codes: A61B 5/36 Detecting PQ interval, PR interval or QT interval
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• 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
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

A device for analyzing a physiological signal, such as an electrocardiogram or electrogram, that was previously collected, filtered, sampled and digitized. The device memorizes the digitized signal and analyzes it by decomposing the signal into a plurality of N parameterized bump functions, where each bump function is a continuous function defined by three successive intervals, respectively, a first monotonic parameterized function, an affine function, and a second monotonic parameterized function, with one of the monotonic parameterized functions being increasing and the other decreasing. The parameterized functions are preferably half-Gaussian functions, and the affine function preferably has a null slope. Each N bump function is classified by recognizing at least one parameter characteristic of each wave, and allotting a standardized label, selected among a plurality of predetermined labels, according to one or to more of the characteristic parameters thus recognized.

Claims

1. A medical device for analyzing a physiological signal having variations that define a monodimensional function, said physiological signal having been collected, filtered, sampled and digitized, comprising:

means for memorizing the digitized physiological signal in a memory, and

processor means for analyzing the memorized physiological signal, comprising;

extracting means for decomposing the signal into a plurality of N parameterized bump functions, wherein each bump function is a continuous function having three successive intervals including a first monotonic parameterized function, an affine function, and a second monotonic parameterized function, one of said first second monotonic parameterized functions being an increasing function and the other being a decreasing function; and

classifying means for recognizing at least one characteristic parameter of each N parameterized bump function, and allotting to said bump function a standardized label selected from among a plurality of predetermined labels, according to said at least one recognized characteristic parameter;

means for storing the standardized label into memory;

analyzing means for extracting abnormal signals indicating a pathological event or rhythm disorder from the analysis of said physiological signal;

means for transferring said abnormal signals to a computer display such that a physician can make a diagnosis of a condition based on said abnormal signals.

2. The device of claim 1 wherein said device comprises an active medical device and said signal is a physiological signal collected by said device.

3. The device of claim 1 wherein said monodimensional function comprises a temporal dimension.

4. The device of claim 3 wherein said signal is an electrocardiographic signal forming a wave of the PQRST type.

5. The device of claim 4 wherein said analyzing means further comprises a subtracting means for withdrawing from the memorized signal at least one of the determined N parameterized bump functions carrying an allotted label.

6. The device of claim 4 wherein said N parameterized bump functions further comprise five bump functions.

7. The device of claim 6 wherein said predetermined labels comprise the P, Q, R, S and T waves of said electrocardiographic signal.

8. The device of claim 7 wherein the device further comprises means for determining a variability over time of at least one specific factor of at least one of the determined N parameterized bump functions.

9. The device of claim 8 wherein said specific factor is selected from among an amplitude of the T wave, a temporal interval between the QRS wave and the T wave, a PR interval, an amplitude of the P wave, and a direction of a significant axis determined by a PCA analysis.

10. The device of claim 7 further comprising means for determining a temporary correlation of a specific factor between at least two of said determined N parameterized bump functions.

11. The device of claim 10 wherein said specific factor is selected from among an amplitude of the T wave, a temporal interval between the QRS wave and the T wave, a PR interval, an amplitude of the P wave, and a direction of a significant axis determined by a PCA analysis.

12. The device of claim 4 wherein said electrocardiographic signal comprises a signal obtained by a PCA analysis and projection of the principal components on a significant axis.

13. The device of claim 12 wherein said significant axis comprises a dynamically computed axis of maximum amplitude.

14. The device of claim 1 wherein said affine function is a function having a null slope.

15. The device of claim 1 wherein each of said monotonic parameterized functions comprises a half-Gaussian function.

16. The device of claim 1 wherein said affine function is a function having a null slope and said first and second monotonic parameterized functions are each a half-Gaussian function, and said characteristic parameters further comprise a set of five parameters selected from among the standard deviation of each of the two half-Gaussian functions, a definition interval length of the affine function, an ordinate position of said interval, and a peak amplitude of said half-Gaussian function.

17. The device of claim 1 wherein said extracting means further comprises:

a library containing a plurality of predetermined bump types;

means for selecting from said library, for each of said N parameterized bump functions, a bump type that is the most relevant in regard to the signal to be decomposed; and

means for adapting the parameters of each of the N selected bump-types and minimizing a variation between the signal and the composition of the parameterized N bump-types.

18. The device of claim 17 wherein the selecting means operates by an orthogonalisation of said selected most relevant bump-type.

19. The device of claim 17 wherein said adapting means performs a non-linear optimization under constraints of said parameters.

20. The device of claim 1 wherein said classifying means further comprises means for implementing hidden Markov models.