PATIENT STATE DETECTION BASED ON SUPPORT VECTOR MACHINE BASED ALGORITHM

• US 20100280334A1
• Filed: 01/26/2010
• Published: 11/04/2010
• Est. Priority Date: 04/30/2009
• Status: Abandoned Application

• Current Assignee: Medtronic Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Incorporated (Medtronic Plc)
• Inventors: Denison, Timothy J., Carlson, David L., Shoeb, Ali H.
• Application Number:
  US12/694,035
• Publication Number:
  US 20100280334A1
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61N 1/36082 Cognitive or psychiatric applications, e.g. dementia or Alzheimer's disease
• CPC Class Codes: G06F 18/241 relating to the classification model, e.g. parametric or non-parametric approaches
• CPC Class Codes: G06F 2221/2101 Auditing as a secondary aspect
• CPC Class Codes: G06F 2221/2105 Dual mode as a secondary aspect
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A patient state is detected with at least one classification boundary generated by a supervised machine learning technique, such as a support vector machine. In some examples, the patient state detection is used to at least one of control the delivery of therapy to a patient, to generate a patient notification, to initiate data recording, or to evaluate a patient condition. In addition, an evaluation metric can be determined based on a feature vector, which is determined based on characteristics of a patient parameter signal, and the classification boundary. Example evaluation metrics can be based on a distance between at least one feature vector and the classification boundary and/or a trajectory of a plurality of feature vectors relative to the classification boundary over time.

Claims

1. A method comprising:

generating a signal based on a sensed parameter of a patient;

determining a plurality of feature vectors over time based on the signal;

applying a support vector machine based algorithm to classify a patient state based on the plurality of feature vectors, wherein the support vector machine algorithm based algorithm defines a classification boundary in a feature space;

determining a trajectory of the feature vectors within the feature space relative to the classification boundary; and

generating an indication based on the trajectory of the feature vectors within the feature space.

2. The method of claim 1, wherein generating the indication comprises determining an evaluation metric for the patient state based on the trajectory of the feature vectors within the feature space.

3. The method of claim 2, wherein determining the evaluation metric comprises determining the evaluation metric based on a number of feature values approaching the classification boundary within a predetermined range of time.

4. The method of claim 2, wherein determining the evaluation metric comprises:

determining a distance between at least one of the feature values and the classification boundary; and

determining the evaluation metric based on the distance.

5. The method of claim 4, wherein the distance comprises at least one of a mean, median or lowest distance of distances between at least two of the feature values and the classification boundary.

6. The method of claim 4, wherein the distance comprises the distance between the classification boundary and the feature value determined based on a most recent segment of the signal.

7. The method of claim 2, wherein determining the evaluation metric comprises determining a number of consecutive feature vectors that define a trajectory towards the classification boundary.

8. The method of claim 2, wherein determining the evaluation metric comprises determining a number of feature vectors within the trajectory that are less than a threshold distance away from the classification boundary.

9. The method of claim 1, further comprising controlling delivery of therapy to the patient based on the indication.

10. The method of claim 9, wherein controlling delivery of therapy to the patient comprises at least one of deactivating, activating or adjusting therapy delivery to the patient if the feature vectors define the trajectory toward the classification boundary over time.

11. The method of claim 10, wherein at least one of deactivating, activating or adjusting therapy delivery to the patient if the feature vectors define the trajectory toward the classification boundary over time comprises at least one of deactivating, activating or adjusting therapy delivery to the patient if a threshold number of feature vectors for consecutive segments of the signal define the trajectory toward the classification boundary over time.

12. The method of claim 9, wherein controlling delivery of therapy to the patient based on the trajectory of the feature vectors within the feature space comprises at least one of activating, deactivating or adjusting therapy delivery to the patient if the feature vectors define the trajectory away from the classification boundary over time.

13. The method of claim 12, wherein at least one of deactivating, activating or adjusting therapy delivery to the patient if the feature vectors define the trajectory away from the classification boundary over time comprises at least one of deactivating, activating or adjusting therapy delivery to the patient if a threshold number of feature vectors for consecutive segments of the signal define the trajectory away from the classification boundary over time.

14. The method of claim 9, further comprising determining a distance between each of the feature vectors and the classification boundary, wherein controlling delivery of therapy to the patient based on the trajectory of the feature vectors within the feature space comprises at least one of deactivating, activating or adjusting therapy delivery to the patient when a value based on the distances between the feature vectors and classification boundary is less than or equal to a threshold value.

15. The method of claim 14, wherein the value comprises at least one of a mean or a median distance based on distances of at least two feature vectors and the classification boundary, or a smallest distance between one of the feature vectors and the classification boundary.

16. The method of claim 1, wherein the patient state comprises a posture state.

17. The method of claim 1, wherein the patient state comprises at least one of a seizure state, a movement disorder states or a mood state.

18. The method of claim 1, wherein the parameter comprises a least one of patient motion or activity, heart rate, respiratory rate, electrodermal activity, thermal activity or muscle activity.

19. A system comprising:

a sensing module that generates a signal indicative of a parameter of the patient; and

a processor that receives the signal, determines a plurality of feature vectors over time based on the signal, applies a support vector machine based algorithm to classify a patient state based on the plurality of feature vectors, wherein the support vector machine algorithm based algorithm defines a classification boundary in a feature space, determines a trajectory of the feature vectors within the feature space relative to the classification boundary, and generates an indication based on the trajectory of the feature vectors within the feature space.

20. The system of claim 19, further comprising a memory, wherein the processor generates the indication by at least determining an evaluation metric for the patient state based on the trajectory of the feature vectors within the feature space and stores the evaluation metric in the memory.

21. The system of claim 20, wherein the processor determines the evaluation metric based on a number of feature values approaching the classification boundary within a predetermined range of time.

22. The system of claim 20, wherein the processor determines the evaluation metric by at least determining a distance between at least one of the feature values and the classification boundary, and determining the evaluation metric based on the distance.

23. The system of claim 22, wherein the distance comprises at least one of a mean, median or lowest distance of distances between at least two of the feature values and the classification boundary.

24. The system of claim 22, wherein the distance comprises the distance between the classification boundary and the feature value determined based on a most recent segment of the signal.

25. The system of claim 20, wherein the processor determines the evaluation metric by at least determining a number of consecutive feature vectors that define a trajectory towards the classification boundary.

26. The system of claim 20, wherein the processor determines the evaluation metric by at least determining a number of feature vectors within the trajectory that are less than a threshold distance away from the classification boundary.

27. The system of claim 19, further comprising a therapy module, wherein the processor controls delivery of therapy to the patient by the therapy module based on the trajectory of the feature vectors within the feature space.

28. The system of claim 27, wherein the processor controls delivery of therapy to the patient by the therapy module by at least one of deactivating, activating or adjusting therapy delivery to the patient if the feature vectors define the trajectory toward the classification boundary over time.

29. The system of claim 27, wherein the processor controls delivery of therapy to the patient by the therapy module by at least one of deactivating, activating or adjusting therapy delivery to the patient if the feature vectors define the trajectory away from the classification boundary over time.

30. The system of claim 27, wherein the processor determines a distance between each of the feature vectors and the classification boundary, and controls delivery of therapy to the patient by the therapy module by at least one of deactivating, activating or adjusting therapy delivery to the patient when a value based on the distances between the feature vectors and classification boundary is less than or equal to a threshold value.

31. The system of claim 30, wherein the value comprises at least one of a mean or a median distance based on distances of at least two feature vectors and the classification boundary, or a smallest distance between one of the feature vectors and the classification boundary.

32. The system of claim 19, wherein the patient state comprises a posture state.

33. The system of claim 19, wherein the patient state comprises at least one of a seizure state, a movement disorder state or a mood state.

34. The system of claim 19, wherein the patient parameter comprises a least one of patient motion or activity, heart rate, respiratory rate, electrodermal activity, thermal activity or muscle activity.

35. A system comprising:

means for receiving a signal indicative of a parameter of a patient;

means for determining a plurality of feature vectors over time based on the signal;

means for applying a support vector machine based algorithm to classify a patient state based on the plurality of feature vectors, wherein the support vector machine algorithm based algorithm defines a classification boundary in a feature space;

means for determining a trajectory of the feature vectors within the feature space relative to the classification boundary; and

means for generating an indication based on the trajectory of the feature vectors within the feature space.

36. The system of claim 35, further comprising means for determining and storing an evaluation metric for the patient state based on the trajectory of the feature vectors within the feature space.

37. The system of claim 35, further comprising means for controlling delivery of therapy to the patient based on the trajectory of the feature vectors within the feature space.

38. A computer readable medium comprising instructions that cause a programmable processor to:

receive a signal indicative of a parameter of a patient;

determine a plurality of feature vectors over time based on the signal;

apply a support vector machine based algorithm to classify a patient state based on the plurality of feature vectors, wherein the support vector machine algorithm based algorithm defines a classification boundary in a feature space;

determine a trajectory of the feature vectors within the feature space relative to the classification boundary; and

generate an indication based on the trajectory of the feature vectors within the feature space.

39. The computer readable medium of claim 38, further comprising instructions that cause a programmable processor to determine an evaluation metric for the patient state based on the trajectory of the feature vectors within the feature space.

40. The computer readable medium of claim 38, further comprising instructions that cause a programmable processor to control delivery of therapy to the patient based on the trajectory of the feature vectors within the feature space.