Method and device for multimodal neurological evaluation

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

providing a processing device operatively connected to a memory device storing a reference database of clinical results collected from two or more neurocognitive and/or clinical symptoms assessments performed on a plurality of individuals in the presence or absence of brain abnormalities, the processing device comprising a processor configured to perform the steps of:

extracting quantitative features from the clinical results;

storing the extracted features in a pool of selectable features;

selecting a subset of features from the pool of selectable features;

constructing a multimodal classifier using the subset of features, wherein the subset of features includes features extracted from at least two different types of assessments; and

determining classification accuracy of the multimodal 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 multimodal classifier are selected.

3. The method of claim 1 , wherein the neurocognitive assessment is performed using a dynamic questionnaire designed to change questions based on answers provided to prior questions.

4. The method of claim 1 , wherein the neurocognitive assessment is performed using a standardized questionnaire.

5. The method of claim 1 , wherein the quantitative features include one or more of severity ranking of symptoms, a binary metric indicating presence or absence of symptoms, and a binary metric indicating normalized combinations of symptoms.

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

7. The method of claim 6 , wherein the evolutionary algorithm applied is a genetic algorithm.

8. The method of claim 7 , 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.

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

10. The method of claim 9 , 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.

11. The method of claim 1 , wherein the multimodal classifier is Linear Discriminant Function.

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

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

14. The method of claim 13 , wherein the objective function used is Area Under the Receiver Operating Curve of the classifier.

15. The method of claim 1 , wherein the multimodal classifier is a binary classifier.

16. The method of claim 1 , further comprising the step of inputting values for missing clinical results of the plurality of individuals in the population reference database.

17. The method of claim 1 , wherein the database further comprises clinical results from physiological tests.

18. The method of claim 1 , wherein the reference database further comprises clinical results from neurophysiological assessments.

19. The method of claim 1 , wherein the reference database comprises clinical results from brain electrical signals measurements.

20. The method of claim 1 , wherein the clinical symptoms assessment is performed by at least one of a physical examination of the patient and conversation with the patient.