Artifact detection in encephalogram data using an event model

• US 6,591,132 B2
• Filed: 11/30/2001
• Issued: 07/08/2003
• Est. Priority Date: 11/30/2001
• Status: Expired due to Term

• Current Assignee: Natus Medical Incorporated
• Original Assignee: Stellate Systems Inc. (Natus Medical Incorporated)
• Inventors: Agarwal, Rajeev, Gotman, Jean, Flanagan, Danny, Wang, YunHua
• Primary Examiner(s): Hindenburg, Max F.
• Assistant Examiner(s): Natnithithadha, Navin
• Application Number:
  US09/996,587
• Publication Number:
  US 20030105408A1
• Time in Patent Office: 585 Days
• Field of Search: 600/544, 600/545, 600/300, 600/920, 600/923, 600/924, 600/925
• US Class Current: 600/544
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• 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 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: Y10S 128/92 Computer assisted medical diagnostics
• CPC Class Codes: Y10S 128/923 by comparison of patient data to other data

Abstract

A machine-readable storage medium containing a program element for execution by a computing device for performing spike and artifact detection in EG data. The program element comprises a spike detection module for processing EG data to detect spikes. Each spike is a candidate having a likelihood of being related to a physiological event of interest. The program element further comprises an artifact detection module. The artifact detection module is operative to compute respective models of events manifested by the respective spikes detected by the spike detection module, to use the computed models to determine which spikes among the spikes detected by the spike detection module have a high likelihood of being artifacts, to filter the spikes detected by the spike detection module on the basis of the computed models to produce filtered data and to output the filtered data.

Claims

1. A machine readable storage medium containing a program element for execution by a computing device for performing spike and artifact detection in encephalogram (EG) data, said program element, comprising:

a) a spike detection module for processing EG data to detect spikes, each spike being a candidate having a likelihood of being related to a physiological event of interest;

b) an artifact detection module, said artifact detection module being operative for;

i) spikes detected by said spike detection module, computing respective models of events manifested by the respective spikes;

ii) use the computed models to determine which spikes among the spikes detected by said spike detection module are highly likely to be artifacts;

iii) filtering the spikes detected by said spike detection module on the basis of the computed models to produce filtered data; and

iv) outputting the filtered data.

2. A machine readable storage medium as defined in claim 1 wherein computation of at least one of the respective models includes computing a dipole equivalent to the event.

3. A machine readable storage medium as defined in claim 2 wherein computation of at least one of the respective models includes computing a location of the dipole equivalent.

4. A machine readable storage medium as defined in claim 2 wherein computation of at least one of the respective models includes computing a time instant at which the dipole model is to be computed.

5. A machine readable storage medium as defined in claim 4 wherein computation of at least one of the respective models includes computing a time instant at which the dipole model is to be computed without user'"'"'s intervention.

6. A machine readable storage medium as defined in claim 3 wherein computation of at least ones of the respective models includes computing the location of the dipole equivalent in a three-dimensional space.

7. A machine readable storage medium as defined in claim 3 wherein computation of at least one of the respective models includes computing the location of the dipole equivalent in a two-dimensional space.

8. A machine readable storage medium as defined in claim 2 wherein computation of at least one of the respective models includes computing an eccentricity of the dipole equivalent.

9. A machine readable storage medium as defined in claim 2 wherein computation of at least ones of the respective model includes computing a residual variance of the dipole equivalent.

10. A machine readable storage medium as defined in claim 1 wherein the physiological event of interest is related to an epileptiform transient.

11. A machine readable storage medium as defined in claim 1 wherein the physiological event of interest is related to a sleep transient.

12. A machine readable storage medium as defined in claim 1 wherein said artifact detection module is operative to determine whether a spike is highly likely to be an artifact at least in part on a basis of a location of a dipole equivalent of the event manifested by the spike.

13. A machine readable storage medium as defined in claim 1 wherein said artifact detection module is operative to determine whether a spike is highly likely to be an artifact at least in part on a basis of an eccentricity of a dipole equivalent of the event manifested by the spike.

14. A machine readable storage medium as defined in claim 1 wherein said artifact detection module is operative to determine whether a spike is highly likely to be an artifact at least in part on a basis of a residual variance of a dipole equivalent of the event manifested by the spike.

15. A machine readable storage medium as defined in claim 1 wherein the EG data is electroencephalogram (EEG) data.

16. A machine readable storage medium as defined in claim 1 wherein the EG data is magnetoencephalogram (MEG) data.

17. A machine readable storage medium containing a program element for execution by a computing device for performing artifact detection in EG data, said program element, comprising:

a) an input for receiving data derided from an EG and representative of spikes, each spike being a candidate having a likelihood of being related to a physiological event of interest;

b) an artifact detection module, said artifact detection module receiving the data representative of spikes and being operative for;

i) computing respective models of events manifested by the respective spikes;

ii) use the computed models to determine which spikes among the spikes detected by said detection module are highly likely to be artifacts;

iii) outputting data allowing to distinguish in the data representative of spikes the spikes determined to highly likely be artifacts.

18. A machine readable storage medium as defined in claim 17 wherein computation of at least one of the respective models includes computing a dipole equivalent to the event.

19. A machine readable storage medium as defined in claim 18 wherein computation of at least one of the respective models includes computing a location of the dipole equivalent.

20. A machine readable storage medium as defined in claim 18 wherein computation of at least one of the respective models includes computing a time instant at which the dipole model is to be computed.

21. A machine readable storage medium as defined in claim 20 wherein computation of at least one of the respective models includes computing a time instant at which the dipole model is to be computed without user'"'"'s intervention.

22. A machine readable storage medium as defined in claim 19 wherein computation of at least one of the respective models includes computing the location of the dipole equivalent in a three-dimensional space.

23. A machine readable storage medium as defined in claim 19 wherein computation of at least one of the respective models includes computing the location of the dipole equivalent in a two-dimensional space.

24. A machine readable storage medium as defined in claim 18 wherein computation of at least one of the respective models includes computing an eccentricity of the dipole equivalent.

25. A machine readable storage medium as defined in claim 18 wherein computation of at least one of the respective model includes computing a residual variance of the dipole equivalent.

26. A machine readable storage medium as defined in claim 17 wherein the physiological event of interest is related to an epileptiform transient.

27. A machine readable storage medium as defined in claim 17 wherein the physiological event of interest is related to a sleep transient.

28. A machine readable storage medium as defined in claim 17 wherein said artifact detection module is operative to determine whether a spike is highly likely to be an artifact at least in part on a basis of a location of a dipole equivalent of the event manifested by the spike.

29. A machine readable storage medium as defined in claim 17 wherein said artifact detection module is operative to determine whether a spike is highly likely to be an artifact at least in part on a basis of an eccentricity of a dipole equivalent of the event manifested by the spike.

30. A machine readable storage medium as defined in claim 17 wherein said artifact detection module is operative to determine whether a spike is highly likely to be an artifact at least in part on a basis of a residual variance of a dipole equivalent of the event manifested by the spike.

31. A machine readable storage medium as defined in claim 17 wherein the EG data is electroencephalogram (EEG) data.

32. A machine readable storage medium as defined in claim 17 wherein the EG data is magnetoencephalogram (MEG) data.

33. A method for performing artifact detection in EG data, comprising:

a) receiving data derived from an EG and representative of spikes, each spike being a candidate having a likelihood of being related to a physiological event of interest;

i) computing, from the data derived from an EG, respective models of events manifested by the respective spikes;

ii) using the computed models to determine which spikes among the spikes detected by said detection module are likely to be artifacts;

iii) outputting data allowing to distinguish in the data representative of spikes the spikes determined to likely be artifacts.