Method and system for determining a variation in a metabolic function and managing the variation accordingly

• US 8,880,164 B2
• Filed: 04/20/2010
• Issued: 11/04/2014
• Est. Priority Date: 04/20/2009
• Status: Active Grant

• Current Assignee: University of Technology at Sydney
• Original Assignee: University of Technology at Sydney
• Inventors: Nguyen, Hung Tan
• Primary Examiner(s): BERHANU, ETSUB D
• Application Number:
  US13/265,400
• Publication Number:
  US 20120095359A1
• Time in Patent Office: 1,659 Days
• Field of Search: 600/544
• US Class Current: 600/544
• CPC Class Codes: A61B 5/374 Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes 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: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61F 2/022 Artificial gland structures using bioreactors hemodialysis A61M1/16; hemofiltration A61M1/34; other extracorporeal blood treatment A61M1/36
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

This invention describes a method for determining an abnormality of metabolic function and/or a variation of metabolic function, the method including the steps of, in a processing system receiving electroencephalography (EEG) signal information (100), analyzing the signal information (110), and determining the abnormality from the analysis (120). It also describes a method for managing the variation accordingly, the method including the steps of, detecting the abnormality (400), applying patient information (410), determining appropriate dose (420), and providing the required dose (430).

Claims

1. A method for determining an indication of the presence of hypoglycaemia, the method including the steps of, in a processing system:

a) receiving electroencephalography (EEG) signal information, including;

(i) an alpha wave; and

(ii) a theta wave;

b) analysing the signal information;

c) determining a variation from the analysis for determining an indication of the presence of hypoglycaemia, the variation including;

(i) a slowing of alpha wave activity and a shift in a centroid alpha frequency; and

,

(ii) a quickening of theta wave activity and a shift in a centroid theta frequency; and

,

d) determining an indication of the presence of hypoglycaemia from the variation.

2. The method of claim 1, wherein the method includes receiving signal information in a continuously moving time window.

3. The method of claim 2, wherein the method provides for a substantially real-time detection of the variation.

4. The method of claim 1, wherein the received EEG signal information further includes

a delta wave.

5. The method of claim 4, wherein analysing the signal information includes any one or a combination of:

(i) determining a shift in a centroid delta frequency;

(ii) determining a rate of change of a alpha wave frequency;

(iii) determining a rate of change of the theta wave frequency; and

,

(iv) determining a rate of change in the delta wave frequency.

6. The method of claim 1, wherein the method includes receiving the EEG signal information from any one or a combination of the occipital cortex and parietal cortex.

7. The method of claim 1, wherein the method includes providing an indication of hypoglycaemia by receiving information, including an increase in delta wave activity of the EEG signal.

8. The method of claim 7, wherein the method further includes determining an average time constant of hypoglycaemic episodes and a natural time constant related to normal sleep patterns, for detecting hypoglycaemia during sleep.

9. The method of claim 1, wherein the method includes performing a data mining technique on the signal information to determine an abnormality of metabolic function and/or a variation of metabolic function.

10. The method of claim 9, wherein the method includes applying an artificial or optimised Bayesian neural network.

11. The method of claim 9, wherein the method includes training data in relation to EEG signals, associated with a particular patient.

12. The method of claim 1, wherein the method includes:

a) estimating a patient model based on an EEG response; and

,

b) calculating an appropriate medicament dose for managing the variation.

13. The method of claim 12, wherein the method includes administering the dose by any one or a combination of:

a) a pump;

b) an oral device;

c) a medicament dispenser; and

d) an intravenous device.

14. The method of claim 12, wherein the method includes generating a signal to cause an automatic administration of the medicament dose.

15. The method of claim 12, wherein the variation is an indication of hypoglycaemia and the method includes calculating an appropriate glycogen/glucose dose.

16. The method of claim 1, wherein the method includes sounding an alarm when a variation is determined.

17. An artificial pancreas for managing hypoglycaemia, the artificial pancreas including:

a) a processing system, the processing system being configured to determine an indication of the presence of hypoglycaemia in accordance with the method of claim 1; and

,

b) an administration device, the administration device being configured to administer an appropriate dose of glucose/glycogen to manage hypoglycaemia.

18. The artificial pancreas of claim 17, wherein the pancreas is capable of being used as an overnight device.

19. The artificial pancreas of claim 17, wherein the pancreas is at least partially implantable.

20. A system for determining an indication of the presence of hypoglycaemia, the system including a processing system, the processing system being configured to:

a) receive electroencephalography (EEG) signal information, including;

(i) an alpha wave; and

(ii) a theta wave;

b) analyse the signal information;

c) determine a variation from the analysis for determining an indication of the presence of hypoglycaemia, the variation including;

(i) a slowing of alpha wave activity and a shift in a centroid alpha frequency; and

,

(ii) a quickening of theta wave activity and a shift in a centroid theta frequency; and

,

d) determine an indication of the presence of hypoglycaemia from the variation.

21. The system of claim 20, wherein the processing system is adapted to receive EEG signal information from a wireless headset.