METHOD AND DEVICE FOR MULTIMODAL NEUROLOGICAL EVALUATION

• US 20130184603A1
• Filed: 01/18/2012
• Published: 07/18/2013
• Est. Priority Date: 01/18/2012
• Status: Active Grant

• Current Assignee: Brainscope SPV LLC
• Original Assignee: Neil S. Rothman
• Inventors: ROTHMAN, Neil S.
• Granted Patent: US 8,792,974 B2
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/378 Visual stimuli
• CPC Class Codes: A61B 5/6803 Head-worn items, e.g. helmets, masks, headphones or goggles
• 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 5/7475 User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: F04C 2270/041 Controlled or regulated
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method of building classifiers for multimodal neurological assessment is described. The method comprises the steps of extracting quantitative features from a plurality of physiological and neurocognitive assessments, and selecting a subset of features from the extracted pool of features to construct multimodal classifiers. A device for performing point-of-care multimodal neurological assessment is also described.

Claims

1. A method of building a classifier for multimodal assessment of a subject'"'"'s neurological condition, comprising the steps of:

providing a signal processing device operatively connected to a memory device storing results of two or more different assessments performed on a plurality of individuals in the presence or absence of brain abnormalities, the signal processing device comprising a processor configured to perform the steps of;

extracting quantitative features from the results of the two or more different assessments;

storing the extracted features in a pool of selectable features;

selecting a subset of features from the pool of selectable features to construct the classifier; and

determining classification accuracy of the classifier by using it to classify data records having a priori classification information.

2. The method of claim 1, further comprising the step of applying one or more data reduction criteria to the pool of selectable features to create a reduced pool of features from which the subset of features for constructing the classifier are selected.

3. The method of claim 1, wherein the two or more different assessments comprise neurophysiological and neurocognitive assessments.

4. The method of claim 3, wherein the neurophysiological assessments comprise recording of brain electrical signals.

5. The method of claim 3, wherein the two or more different assessments comprise one or more types of reaction time measurements.

6. The method of claim 3, wherein the neurocognitive assessment is performed using a dynamic questionnaire designed to change questions based on answers provided to prior questions.

7. The method of claim 1, wherein the two or more different assessments comprise measurement of various physiological parameters.

8. The method of claim 7, wherein the two or more assessments comprise recording of electrocardiographic signals.

9. The method of claim 1, wherein the subset of features are selected using an evolutionary algorithm.

10. The method of claim 9, wherein the evolutionary algorithm applied is a genetic algorithm.

11. The method of claim 10, wherein the selected subset of features is optimized using at least one of a Random Mutation Hill Climbing algorithm and a Modified Random Mutation Hill Climbing algorithm.

12. The method of claim 1, wherein the subset of features is selected using a Simple Feature Picker algorithm

13. The method of claim 12, wherein the selected subset of features is optimized using at least one of a Random Mutation Hill Climbing algorithm and a Modified Random Mutation Hill Climbing algorithm.

14. The method of claim 1, wherein the classifier is a Linear Discriminant Function.

15. The method of claim 1, wherein the classifier is a Quadratic Discriminant Function.

16. The method of claim 1, wherein an objective function is used to evaluate the performance of the classifier.

17. The method of claim 16, wherein the objective function used is Area Under the Receiver Operating Curve of the classifier.

18. A method of building a classifier for classification of individual data into one of two or more categories of neurological condition, the method comprising the steps of:

providing a processor configured to build a classifier;

providing a memory device operatively coupled to the processor, the memory device storing a population reference database comprising a pool of quantitative features extracted from the results of physiological and neurocognitive assessments performed on a plurality of individuals in the presence or absence of brain abnormalities;

selecting a plurality of brain electrical signal features from the pool of quantitative features in the population reference database;

selecting another plurality of quantitative features derived from one or more additional assessments performed on the plurality of individuals in the population reference database;

constructing a classifier using the selected quantitative features;

evaluating performance of the classifier using pre-labeled data records stored in the memory device, wherein the pre-labeled data records are assigned a priori to one of the two or more categories.

19. The method of claim 18, wherein the one or more additional assessments comprise assessment of reaction time.

20. The method of claim 18, wherein the one or more additional assessments comprise recording of electrocardiographic signals.

21. The method of claim 18, further comprising the step of applying one or more data reduction criteria to the selected features to create a reduced subset of features from which the classifier is constructed.

22. The method of claim 18, wherein the quantitative features are selected using an evolutionary algorithm.

23. The method of claim 22, wherein the evolutionary algorithm applied is a genetic algorithm.

24. The method of claim 23, wherein the selection of features is optimized using at least one of a Random Mutation Hill Climbing algorithm and a Modified Random Mutation Hill Climbing algorithm.

25. The method of claim 18, wherein the quantitative features are selected using a Simple Feature Picker algorithm.

26. The method of claim 25, wherein the selection of features is optimized using at least one of a Random Mutation Hill Climbing algorithm and a Modified Random Mutation Hill Climbing algorithm.

27. The method of claim 18, wherein the classifier is a Discriminant Function.

28. The method of claim 27, wherein the classifier is a Quadratic Discriminant Function.

29. The method of claim 27, wherein the classifier is a Linear Discriminant Function.

30. The method of claim 18, wherein an objective function is used to evaluate the performance of the classifier.

31. The method of claim 18, wherein the objective function used is Area Under the Receiver Operating Curve of the binary classifier.

32. The method of claim 18, wherein the classifier is a binary classifier.

33. The method of claim 18, wherein the classifier is a multiclass classifier.

34. The method of claim 18, wherein a series of binary classifiers is used to classify individual data into one of three or more categories.

35. A device for performing multimodal assessment of a subject'"'"'s neurological condition, the device comprising:

a headset comprising one or more neurological electrodes configured to record the subject'"'"'s brain electrical activity;

an input device configured to acquire results of one or more physiological and neurocognitive assessments performed on the subject;

a base unit operatively coupled to the headset and the input device, the base unit comprising a processor configured to perform the steps of;

extracting quantitative features from the brain electrical activity data;

extracting quantitative features from the results of the one or more physiological and neurocognitive assessments performed on the subject; and

applying a multimodal classifier to perform classification of the subject'"'"'s neurological condition into one of two or more categories.

36. The method of claim 35, wherein the processor is configured to perform automatic identification and removal of artifacts from the brain electrical activity data.

37. The method of claim 35, wherein the results of the one or more physiological and neurocognitive assessments are entered manually into the input device by a user.

38. The method of claim 35, wherein the results of the one or more physiological and neurocognitive assessments are acquired by the input device wirelessly or through a wired connection.

39. The device of claim 35, further comprising a memory device for storing interactive instructions for using and operating the device.

40. The device of claim 39, wherein the memory device stores a dynamic questionnaire that provides step-by-step instructions for performing the multimodal assessment.

41. The device of claim 35, further comprising a touch-screen interface to enter user input.

42. The device of claim 35, wherein the base unit is a handheld device.

43. The device of claim 35, wherein the one or more physiological and neurocognitive assessments comprise reaction time measurement.

44. The device of claims 35, wherein the one or more physiological and neurocognitive assessments comprise recording of electrocardiographic signals.

45. The device of claim 35, wherein the multimodal classifier comprises one or more discriminant functions.

46. The device of claim 35, wherein the multimodal classifier is a binary classifier.

47. The device of claim 35, wherein the multimodal classifier is a multi-class classifier.

48. The device of claim 35, further comprising a monitor for providing visual stimuli to the subject to elicit visual evoked potentials.

49. The device of claim 35, further comprising a pair of goggles for providing visual stimuli to the subject to elicit visual evoked potentials.