APPARATUS AND METHOD FOR SUPPORTING REHABILITATION OF BRAIN-DAMAGED PATIENT

• US 20150325139A1
• Filed: 04/22/2015
• Published: 11/12/2015
• Est. Priority Date: 05/09/2014
• Status: Abandoned Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: KANG, Hyo A, KAM, Hye Jin
• Application Number:
  US14/693,703
• Publication Number:
  US 20150325139A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2505/09 Rehabilitation or training
• CPC Class Codes: A61B 2576/026 for the brain
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/4064 Evaluating the brain A61B5/031, A61B5/369, A61B5/14553 take 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 5/7271 Specific aspects of physiological measurement analysis A61B5/316, A61B5/349 and A61B5/374 take precedence
• CPC Class Codes: G09B 19/00 Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26
• CPC Class Codes: G09B 5/00 Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/70 relating to mental therapies, e.g. psychological therapy or autogenous training
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

An apparatus that supports a rehabilitation of a brain-damaged patient, including a patient condition analyzer configured to generate a patient condition scenario about a brain condition of a patient based on brain-related information of the patient, a network model determiner configured to determine a network model to be applied to the patient using the patient condition scenario, and a rehabilitation model generator configured to generate a rehabilitation model to be applied to the patient based on the determined network model.

Claims

1. An apparatus that supports a rehabilitation of a brain-damaged patient, the apparatus comprising:

a patient condition analyzer configured to generate a patient condition scenario about a brain condition of a patient based on brain-related information of the patient;

a network model determiner configured to determine a network model to be applied to the patient using the patient condition scenario; and

a rehabilitation model generator configured to generate a rehabilitation model to be applied to the patient based on the determined network model.

2. The apparatus of claim 1, wherein the rehabilitation model generator is further configured to generate the rehabilitation model to be applied to the patient based on a rehabilitation efficiency mode.

3. The apparatus of claim 1, wherein the patient condition analyzer is further configured to extract a feature value for a primary feature by analyzing the brain-related information of the patient, and to generate the patient condition scenario by applying an analytical technique to the extracted feature value.

4. The apparatus of claim 1, wherein the patient condition analyzer is further configured to extract a feature value for a primary feature by analyzing the brain-related information of the patient, and to generate the patient condition scenario by using a patient information item that is acquired based on similarity between a change over time of the extracted feature value and a brain-related disease progression information of the patient stored in a patient information database (DB).

5. The apparatus of claim 1, wherein the network model determiner is further configured to determine at least one of a currently damaged brain area and an anticipated area of further brain damage by using the patient condition scenario, to extract one or more sub network models associated with the determined area from the brain area network model, and to determine at least one of the one or more extracted sub network models as a network model to be applied to the patient.

6. The apparatus of claim 5, wherein the network model determiner is further configured to determine a functional brain change of the determine area using the patient condition scenario, and to assign weight values to the one or more extracted sub network models based on the determined functional brain change, and, based on the assigned weight values, to determine at least one of the one or more extracted sub network models as a network model to be applied to the patient.

7. The apparatus of claim 5, wherein the rehabilitation model generator is further configured to determine a substitute area or a compensating area for a function of the determine area using the determined network model, and to generate a rehabilitation model for rehabilitation of the determined area based on the rehabilitation efficiency model.

8. The apparatus of claim 1, wherein the rehabilitation model generator is further configured to determine a rehabilitation program based on one or more information items in knowledge information and rehabilitation information of patients who are in condition similar to the patient condition scenario, to predict effects that contain a degree of functional recovery expected by applying the determined rehabilitation program, and to generate a rehabilitation model including the determined rehabilitation program and the predicted effects.

9. The apparatus of claim 1, further comprising:

a rehabilitation model provider configured to provide the generated rehabilitation model to a user.

10. A method of supporting a rehabilitation of a brain-damaged patient, the method comprising:

generating a patient condition scenario about a brain condition of a patient based on brain-related information of the patient;

determining a network model to be applied to the patient based on the patient condition scenario; and

generating a rehabilitation model to be applied to the patient based on the determined network model.

11. The method of claim 10, wherein the rehabilitation model is further generated to be applied to the patient based on a rehabilitation efficiency model.

12. The method of claim 10, wherein the generating of a patient condition scenario comprises:

extracting a feature value for one or more primary feature by analyzing the brain-related information of the patient; and

generating the patient condition scenario by applying a predetermined analytic technique to the extracted feature value.

13. The method of claim 10, wherein the generating of a patient condition scenario comprises:

extracting a feature value for one or more primary features by analyzing the brain-related information of the patient; and

generating the patient condition scenario by using one or more patient information items that are acquired based on a change over time of the extracted feature value and similarity between patient'"'"'s brain-related disease progression information stored in the patient information database (DB).

14. The method of claim 10, wherein the determining of a network model comprises:

determining at least one of a currently damaged brain area and an anticipated area of further brain damage by using the patient condition scenario;

extracting one or more sub networks associated with the determined area from the brain area network model; and

determining at least one of the one or more extracted sub network models as a network model to be applied to the patient.

15. The method of claim 14,

wherein the determining of a network model further comprises determining a functional brain change of the determined area using the patient condition scenario, and

wherein the determining as to a network model to be applied to the patient comprises:

assigning a weight value to the one or more extracted sub networks based on the functional brain change of the determined area; and

based on the assigned weight value, determining at least one of the one or more extracted sub network models as a network model to be applied to the patient.

16. The method of claim 14, wherein the generating of a rehabilitation model comprises:

determining a substitute area or a compensating area for a function of the determined region by using the determined network model; and

generating a rehabilitation model for rehabilitation of the determined region based on the rehabilitation efficiency model.

17. The method of claim 14, wherein the generating of a rehabilitation model comprises:

determining a rehabilitation program based on one or more information items in knowledge information and rehabilitation information of patients who are in condition similar to the patient condition scenario, predicting effects that contain a degree of functional recovery expected by applying the determined rehabilitation program, and generating a rehabilitation model including the determined rehabilitation program and the predicted effects.

18. The method of claim 10, further comprising:

providing the generated rehabilitation model to a user.

19. An apparatus for supporting rehabilitation of a brain-damaged patient, the apparatus comprising:

a patient information database (DB) configured to store at least one of disease information and rehabilitation information of brain-damaged patients;

a knowledge information DB configured to store brain-related knowledge information; and

a network model generator configured to generate a brain area network model by analyzing association between brain areas based on information stored in either the patient information DB or the knowledge information DB.

20. The apparatus of claim 19, wherein the network model generator is further configured to generate the brain area network model by determining a substitute area or a compensating area of each brain area and constructing a network between a specific brain area and the determined area.

21. The apparatus of claim 20, wherein the network model generator is further configured to:

determine one or more areas capable of substituting or compensating for one or more functions of each brain area, and generate one or more sub network models for the one or more functions of each brain area; and

generate the brain area network model including the one or more generated sub network model.

22. The apparatus of claim 19, further comprising:

a rehabilitation efficiency model generator configured to generate a rehabilitation efficiency model by analyzing effects of a rehabilitation program for each brain area based on one or more information items stored in the patient information DB and the knowledge information DB.

23. The apparatus of claim 22, wherein the effects of the rehabilitation program comprises one or more of the following:

a degree of functional recovery expected by applying the rehabilitation program;

a participation of the patient about the rehabilitation program;

a satisfaction of the patient about the rehabilitation program, a convenience of the rehabilitation program; and

a psychological and a physical stress that the patient suffers during an application of the rehabilitation program.

24. The apparatus of claim 19, further comprising:

a patient information collector configured to collect the patient'"'"'s rehabilitation information obtained by applying the rehabilitation model to the patient, and to store the collected rehabilitation information in the patient information DB.

25. An apparatus for supporting rehabilitation of a brain-damaged patient, the apparatus comprising:

a modeler configured to generate a brain area network model including association information of brain areas and a rehabilitation model for each brain area by analyzing eat least one of brain-related patient information and brain-related knowledge information; and

an applier configured to, in response to receipt of brain-related information of a new patient, generate a rehabilitation model to be applied to the new patient, by analyzing the brain-related information, the brain area network model, and the rehabilitation model all together.

26. The apparatus of claim 25, wherein the modeler is further configured to generate one or more sub networks for one or more functions of each brain area, and generate the brain area network model including the one or more generated sub networks.

27. The apparatus of claim 25, wherein the modeler is further configured to:

predict effects of the rehabilitation program, which includes a degree of functional recovery expected by applying the rehabilitation program to each brain area; and

generate the rehabilitation efficiency model including the predicted effects of the rehabilitation program.

28. The apparatus of claim 25, wherein the applier is further configured to:

collect the patient'"'"'s rehabilitation information obtained by applying the rehabilitation model to the patient; and

transmit a feedback on the collected rehabilitation information to the modeler.