Method and device for the medical monitoring in real time of a patient from the analysis of electro-encephalograms, application of this method to characterize and to differentiate between physiological or pathologial conditions, and a method for anticipating epileptic seizures in real time

• US 20020095099A1
• Filed: 01/17/2002
• Published: 07/18/2002
• Est. Priority Date: 04/27/2000
• Status: Active Grant

• Current Assignee: Centre National De La Recherche Scientifique
• Original Assignee: Centre National De La Recherche Scientifique
• Inventors: Quyen, Michel Le Van, Martinerie, Jacques, Varela, Francisco, Baulac, Michel
• Granted Patent: US 6,507,754 B2
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/372 Analysis of electroencephalograms
• CPC Class Codes: A61B 5/375 using biofeedback
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: Y10S 128/92 Computer assisted medical diagnostics

Abstract

The invention concerns a device and a method for the detection of changes in dynamic properties of electrical brain activity to characterize and to differentiate between physiological and pathological conditions, or to anticipate epileptic seizures.

Claims

1. A method for the medical monitoring in real time of a patient from the analysis of electroencephalograms comprising the steps of:

constructing a reference dynamics of a normal state in choosing a long EEG segment S_ref recorded during an interval quite distant in time from any problem,

comparing this reference dynamic with the dynamics of distant test segments S_t,

computing the similarities over the entire EEG recordings by sliding the test segment S_t periodically, the corresponding time course providing information about long term changes before an onset.

2. The method of claim 1 wherein constructing a reference dynamics is derived from the sequences of time intervals between positive-going crossings of a fixed threshold, delay vectors defining an m-dimensional embedding of the dynamics being formed from this sequence of intervals.

3. The method of claim 2 wherein a single value decomposition of the m-dimensional embedding space is applied, identifying the optimal space that contains the trajectory.

4. The method of claim 1 wherein the recording is split into non-overlapping consecutive test segments of 25 seconds each.

5. The method of claim 1 wherein a basic “

skeleton”

of reference dynamics is considered, said skeleton being built by a random selection of a sub-set of points, providing an adapted picture X(S_ref) of the reconstruction, extracting the most frequent occupations of the phase space flow.

6. The method of claim 5 wherein the dynamical similarities are estimated between the reference dynamics X(S_ref) and the projection X(S_t) of a 16-dimensional reconstruction of the test segment S_t on the principal axes of the reference dynamics.

7. The method of claim 6, wherein dynamic similarities are estimated in using statistical measure based on the cross correlation integral:

8. The method of claim 7 wherein the following cross-correlation ratio is used:

γ

(S_ref,S_t)=C(S_ref,S_t)/{square root}{square root over (C(S_ref,S_ref)C(S_t,S_t))}

γ

ranging from 0 to 1 and providing a sensitive measure of closeness between two dynamics.

12. Application of the method according to claim 1 to characterize and to differentiate between physiological and pathological conditions.

9. A device for the medical monitoring in real time of a patient from the analysis of electroencephalograms or EEG comprising an amplifier receiving the EEG signals, an analogic/digital multiconverter and a processor delivering an external output and a output warning.

10. The device of claim 9 which is a free-standing device, light and portable by a patient.

11. The device of claims 10 which is miniaturized so that it is implanted sub-cutaneously.

14. The method of claim 13 wherein constructing a reference dynamics is derived from the sequences of time intervals between positive-going crossings of a fixed threshold, delay vectors defining an m-dimensional embedding of the dynamics being formed from this sequence of intervals.

15. The method of claim 14 wherein a single value decomposition of the m-dimensional embedding space is applied, identifying the optimal space that contains the trajectory.

16. The method of claim 13 wherein the recording is split into non-overlapping consecutive test segments of 25 seconds each.

17. The method of claim 13 wherein a basic “

skeleton”

of reference dynamics is considered, said skeleton being built by a random selection of a sub-set of points, providing an adapted picture X(S_ref) of the reconstruction, extracting the most frequent occupations of the phase space flow.

18. The method of claim 17 wherein the dynamical similarities are estimated between the reference dynamics X(S_ref) and the projection X(S_t) of a 16-dimensional reconstruction of the test segment S_t on the principal axes of the reference dynamics.

19. The method of claim 18, wherein dynamic similarities are estimated in using statistical measure based on the cross correlation integral:

20. The method of claim 19 wherein the following cross-correlation ratio is used:

γ

(S_ref,S_t)=C(S_ref,S_t)/C{square root over (C(S_ref,S_ref)C(S_t,S_t))}

γ

ranging from 0 to 1 and providing a sensitive measure of closeness between two dynamics.

21. The method of claim 13 wherein the epileptic monitoring is conducted in ambulatory conditions.

22. The method of claim 13 wherein EEG electrical recordings are taken from the scalp surface.

13. A method for anticipating epileptic seizures in real time comprising the steps of

constructing a reference dynamics of the non-seizure state in choosing a long EEG segment S_ref recorded during an interval quite distant in time from any seizure,

comparing this reference dynamic with the dynamics of distant test segments S_t,

computing the similarities over the entire EEG recordings by sliding the test segment S_t periodically, the corresponding time course providing information about long term changes before a seizure onset.