Methods of Identifying Sleep & Waking Patterns and Uses

• US 20110218454A1
• Filed: 11/16/2009
• Published: 09/08/2011
• Est. Priority Date: 11/14/2008
• Status: Abandoned Application

• Current Assignee: Neurovigil, Inc
• Original Assignee: Neurovigil, Inc
• Inventors: Low, Philip
• Application Number:
  US13/129,185
• Publication Number:
  US 20110218454A1
• 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 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles

Abstract

Traditional analysis of sleep patterns requires several channels of data. This analysis can be useful for customized analysis including assessing sleep quality, detecting pathological conditions, determining the effect of medication on sleep states and identifying biomarkers, and drug dosages or reactions.

Claims

1. A method to assess brain stages in animals comprising:

attaching at least a single electrode to animal;

obtaining data indicative of brainwave activity;

analyzing said data indicative of brain activity; and

determining at least one parameter indicative of sleep state from said analyzing.

2. The method as in claim 1, wherein said obtaining data is received invasively by inserting at least the single electrode into the skull or brain or between the skull and brain of the animal.

3. The method as in claim 1, wherein said obtaining data is received non-invasively by applying at least the single electrode.

4. The method as in claim 3, wherein said obtaining data is received non-invasively by attaching at least a single dry electrode.

5. The method as in claim 3, wherein said obtaining data is received non-invasively by attaching at least a single wet electrode.

6. The method as in claim 2, wherein said obtaining data is received from at least a single channel of EEG.

7. The method as in claim 3, wherein said obtaining data is received from at least a single channel of EEG.

8. The method as in claim 1, wherein said obtaining data is received wirelessly.

9. The method as in claim 1, wherein said analyzing data indicative of brain activity is automated data.

10. The method as in claim 1, wherein said analyzing data indicative of brain activity is manual data.

12. The method as in claim 1, wherein said analyzing data indicative of brain activity comprises the steps of:

computing the spectrogram;

normalizing the spectrogram;

performing an independent or principal component analysis; and

identifying clusters.

13. The method as in claim 1, wherein said analyzing data indicative of brain activity comprises the step of performing a temporal fragmentation analysis.

14. The method as in claim 1, wherein said analyzing data indicative of brain activity comprises the step of performing a preferred frequency analysis.

15. The method as in claim 1, wherein said analyzing data indicative of brain activity comprise the step of performing a spectral fragmentation analysis.

17. The method as in claim 12, further comprising the additional steps of:

a statistical analysis of the preferred frequency space;

or

the fragmentation space;

or

the cluster space;

to define a sleep parameter.

18. The method as in claim 13, further comprising the additional steps of:

a statistical analysis of the preferred frequency space;

or

the fragmentation space;

or

the cluster space;

to define a sleep parameter.

19. The method as in claim 14, further comprising the additional steps of:

a statistical analysis of the preferred frequency space;

or

the fragmentation space;

or the cluster space;

to define a sleep parameter.

20. The method as in claim 15, further comprising the additional steps of:

a statistical analysis of the preferred frequency space;

or, the fragmentation space;

or the cluster space;

to define a sleep parameter.

21. The method as in claim 1, further comprising determining whether the animal is in a sleep or waking state.

23. The method as in claim 18, wherein receiving means is a non-invasive electrode attached to the animal.

11. A method to assess brain stages in animals comprising the steps of:

normalizing the spectrogram at least once, time over frequency;

normalizing the spectrogram at least once, frequency over time; and

determining at least one parameter indicative of sleep state from said analyzing.

16. The method as in claim 11, further comprising the additional steps of:

a statistical analysis of the preferred frequency space;

or

the fragmentation space;

or

the cluster space;

to define a sleep parameter.

22. A non invasive system to obtain and classify brain waves in animals comprising:

receiving means to obtain data indicative of brain wave activity;

a computing means to analyze said data indicative of brain wave activity;

and a processor to determine at least one parameter indicative of sleep or waking state from said analyzing.

24. The method as in claim 22, wherein said parameter indicative of sleep or waking state comprises information indicative of likely drug consumption, reaction, or dosage.

25. A method for determining sleep states in a subject over a period of time comprising;

receiving data indicative of brain activity for a animal over a period of time;

analyzing said data indicative of brain activity;

and classifying said data based on sleep state.

26. An automated system and method to measure the effects of drug consumption of an animal comprising the steps of:

obtain sleep parameters for an untreated animal;

map said sleep parameters for an untreated animal;

obtain sleep parameters for a treated animal;

map said sleep parameters for treated animal; and

compare said parameters for untreated animal to said parameters for treated animal.

27. An automated system and method to determine pathological conditions of an animal comprising the steps of:

obtain sleep parameters for a healthy animal;

map said sleep parameters for a healthy animal;

obtain sleep parameters on an abnormal animal;

map said sleep parameters for abnormal animal; and

compare said parameters for healthy animal to said parameters for abnormal animal.