Device for the medical monitoring in real time of a patient from the analysis of electroencephalograms

• US 6,507,754 B2
• Filed: 01/17/2002
• Issued: 01/14/2003
• Est. Priority Date: 04/27/2000
• Status: Expired due to Fees

• Current Assignee: Centre National De La Recherche Scientifique
• Original Assignee: Centre National De La Recherche Scientifique
• Inventors: Martinerie, Jacques, Varela, Francisco, Baulac, Michel, Le Van Quyen, Michel
• Primary Examiner(s): Schaetzle, Kennedy
• Assistant Examiner(s): DROESCH, KRISTEN L
• Application Number:
  US10/046,696
• Publication Number:
  US 20020095099A1
• Time in Patent Office: 362 Days
• Field of Search: 600/544-545
• 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 device for medically monitoring in real time of a patient from an analysis of electroencephalogram or EEG signals, comprising:

an amplifier configured to receive and amplify the EEG signals;

an analog/digital multiconverter configured to convert the EEG signals into digital data; and

a processor configured to process the digital data and to provide an output and a warning indicative of a medical problem with the patient,

wherein the processor is programmed to perform the following functions;

construct reference dynamics of a normal state of the patient by prerecording a long normal EEG segment S_ref;

compare the reference dynamics with dynamics of a distant test segment S_t; and

compute similarities over the entire prerecorded EEG segment by sliding the test segment S_t periodically over the prerecorded EEG segment so as to provide information about a possible medical onset occurring with the patient,

wherein the programmed processor performs the comparing function by;

building a skeleton of the reference dynamics by randomly selecting a sub-set of points of the reference dynamics so as to provide an adapted reference dynamics picture X(S_ref) of the reference dynamics; and

estimating dynamic similarities between the adapted reference dynamics picture X(S_ref) and a projection X(S_t) of a 16-dimensional reconstruction of the test segment S_t on the principal axes of the reference dynamics,

wherein the dynamic similarities are estimated using a statistical measure based on the following cross correlation integral;

$C<br><br>\left(S_{\mathrm{ref}},S_t\right)=\frac{1}{N_{\mathrm{ref}}<br><br>N_t}<br><br>\underset{i=1,N_{\mathrm{ref}}}{\sum <br><br>}<br><br>\underset{j=1,N_t}{\sum <br><br>}<br><br>\mathrm{\Theta <br><br>}<br><br>\left(<br><br>X_i<br><br>\left(S_{\mathrm{ref}}\right)-X_j<br><br>\left(S_t\right)<br><br>-r\right)$

 

where Θ

is the Heaviside step function, ∥

∥

is the euclidian norm, N_refN_t denotes the number of elements in each set, and r is a distance, and

wherein the following cross-correlation ratio is used;

$\mathrm{\gamma <br><br>}<br><br>\left(S_{\mathrm{ref}},S_t\right)=C<br><br>\left(S_{\mathrm{ref}},S_t\right)/\sqrt{C<br><br>\left(S_{\mathrm{ref}},S_{\mathrm{ref}}\right)<br><br>C<br><br>\left(S_t,S_t\right)}$

where γ

ranges from 0 to 1 and provides a sensitive measure of closeness between two dynamics.

2. The device of claim 1, wherein the device is a free-standing device, light and portable by a patient.

3. The device of claim 2, wherein the device is miniaturized so that it is implanted sub-cutaneously into the patient.

4. The device of claim 1, wherein constructing the reference dynamics is derived from sequences of time intervals between positive crossings of the prerecorded EEG segment over a fixed threshold so as to form delay vectors defining an m-dimensional embedding of the reference dynamics.

5. The device of claim 4, wherein the processor is further programmed to perform the function of:

applying a single value decomposition to the m-dimensional embedding so as to identify an optimal working space including a trajectory corresponding to the m-dimensional embedding.

6. The device of claim 1, wherein the processor is programmed to further perform the function of:

dividing the prerecorded EEG segment into non-overlapping consecutive test segments of 25 seconds each.

7. The device of claim 1, wherein the programmed processor differentiates between physiological and pathological conditions.

8. A device for anticipating epileptic seizures in real time, comprising:

an amplifier configured to receive and amplify EEG signals;

an analog/digital multiconverter configured to convert the EEG signals into digital data; and

a processor configured to process the digital data and to provide an output and a warning indicative of a medical problem with the patient,

wherein the processor is programmed to perform the following functions;

construct reference dynamics of a non-seizure state in of the patient by prerecording a long normal EEG segment S_ref;

compare the reference dynamics with dynamics of a distant test segment S_t;

compute similarities over the entire prerecorded EEG segment by sliding the test segment S_t periodically over the prerecorded EEG segment so as to provide information about a possible seizure onset occurring with the patient,

wherein the programmed processor performs the comparing function by;

building a skeleton of the reference dynamics by selecting a sub-set of points of the reference dynamics so as to provide an adapted reference dynamics picture X(S_ref) of the reconstruction; and

estimating dynamic similarities between the adapted reference dynamics picture X(S_ref) and a projection X(S_t) of a 16-dimensional reconstruction of the test segment S_t on the principal axes of the reference dynamics,

wherein the dynamic similarities are estimated using a statistical measure based on the following cross correlation integral;

$C<br><br>\left(S_{\mathrm{ref}},S_t\right)=\frac{1}{N_{\mathrm{ref}}<br><br>N_t}<br><br>\underset{i=1,N_{\mathrm{ref}}}{\sum <br><br>}<br><br>\underset{j=1,N_t}{\sum <br><br>}<br><br>\mathrm{\Theta <br><br>}<br><br>\left(<br><br>X_i<br><br>\left(S_{\mathrm{ref}}\right)-X_j<br><br>\left(S_t\right)<br><br>-r\right)$

 

where Θ

is the Heaviside step function, ∥

∥

is the euclidian norm, N_refN_t denotes the number of elements in each set, and r is a distance, and

wherein the following cross-correlation ratio is used;

$\mathrm{\gamma <br><br>}<br><br>\left(S_{\mathrm{ref}},S_t\right)=C<br><br>\left(S_{\mathrm{ref}},S_t\right)/\sqrt{C<br><br>\left(S_{\mathrm{ref}},S_{\mathrm{ref}}\right)<br><br>C<br><br>\left(S_t,S_t\right)}$

where γ

ranges from 0 to 1 and provides a sensitive measure of closeness between two dynamics.

9. The device of claim 8, wherein constructing the reference dynamics is derived from sequences of time intervals between positive crossings of the prerecorded EEG segment over a fixed threshold so as to form delay vectors defining an m-dimensional embedding of the reference dynamics.

10. The device of claim 9, wherein the processor is further programmed to perform the function of:

applying a single value decomposition to the m-dimensional embedding so as to identify an optimal working space including a trajectory corresponding to the m-dimensional embedding.

11. The device of claim 8, wherein the processor is further programmed to perform the function of:

dividing the prerecorded EEG segment into non-overlapping consecutive test segments of 25 seconds each.

12. The device of claim 8, wherein epileptic monitoring of epileptic seizures is conducted in ambulatory conditions.

13. The device of claim 8, wherein EEG signals are taken from a scalp surface of the patient.