Collecting gait information for evaluation and control of therapy

• US 10,251,595 B2
• Filed: 02/22/2017
• Issued: 04/09/2019
• Est. Priority Date: 03/24/2006
• Status: Active Grant

• Current Assignee: Medtronic Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Incorporated (Medtronic Plc)
• Inventors: Heruth, Kenneth T., Miesel, Keith A., Molnar, Gregory F.
• Primary Examiner(s): Marlen, Tammie K
• Application Number:
  US15/439,152
• Publication Number:
  US 20170156663A1
• Time in Patent Office: 776 Days
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0031 Implanted circuitry
• CPC Class Codes: A61B 5/031 Intracranial pressure
• CPC Class Codes: A61B 5/1038 Measuring plantar pressure during gait
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/4082 Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/7405 using sound
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7455 characterised by tactile indication, e.g. vibration or electrical stimulation
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: A61N 1/36003 of motor muscles, e.g. for walking assistance
• CPC Class Codes: A61N 1/36067 Movement disorders, e.g. tremor or Parkinson disease stimulating motor muscle A61N1/36003
• CPC Class Codes: A61N 1/36082 : Cognitive or psychiatric applications, e.g. dementia or Alzheimer's disease
• CPC Class Codes: A61N 1/3615 : Intensity
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

Abstract

A medical device delivers a therapy to a patient. The medical device or another device may periodically determine an activity level or gait parameter of the patient, and associate each determined level or parameter with a current therapy parameter set. A value of at least one activity metric is determined for each of a plurality of therapy parameter sets based on the activity levels or parameters associated with that therapy parameter set. Whether the patient is currently experiencing or anticipated to experience gait freeze caused by their neurological disorder, such as Parkinson'"'"'s disease, may also be determined. Gait freeze events may be associated with current therapy parameters and used to determine activity metric values. In some examples, the activity metric associated with certain therapy parameters may be presented to a user.

Claims

1. A method comprising:

monitoring, with an implantable medical device, a physiological signal of a patient based on a signal generated by an implantable sensor within the patient;

predicting, with the implantable medical device, a gait freeze event based on the signal prior to onset of the gait freeze event; and

providing a gait cue indicative of the predicted gait freeze event to the patient.

2. The method of claim 1, wherein providing a gait cue indicative of the predicted gait freeze event to the patient comprises providing, by the implantable medical device, a gait cue indicative of the predicted gait freeze event to the patient.

3. The method of claim 1, wherein the gait cue is a perceivable vibration.

4. The method of claim 1, wherein the gait cue is an electrical stimulus.

5. The method of claim 1, wherein the gait cue is a visual prompt.

6. The method of claim 1, wherein the gait cue is an audible stimulus.

7. The method of claim 1,

wherein the signal generated by the implantable sensor includes an electromyogram (EMG) signal, and

wherein predicting, with the implantable medical device, the gait freeze event based on the signal includes predicting the gait freeze event based on irregular EMG activity in the EMG signal.

8. The method of claim 1,

wherein the signal generated by the implantable sensor includes an electroencephalogram (EEG) signal, and

wherein predicting, with the implantable medical device, the gait freeze event based on the signal includes predicting the gait freeze event based on irregular EEG activity in the EEG signal.

9. The method of claim 1, wherein predicting, with the implantable medical device, the gait freeze event based on the signal includes:

monitoring a gait of the patient based on the signal generated by the implantable sensor; and

anticipating, with the implantable medical device, the gait freeze event based on the signal.

10. The method of claim 1, wherein predicting, with the implantable medical device, the gait freeze event based on the signal includes:

monitoring a gait of the patient based on the signal generated by the implantable sensor; and

detecting, with the implantable medical device, the gait freeze event based on the signal.

11. The method of claim 1, wherein monitoring the physiological signal of the patient includes monitoring the physiological signal of the patient with one or more electrodes of the implantable sensor.

12. The method of claim 1, wherein monitoring the physiological signal of the patient includes sensing activity via an activity sensor.

13. A system comprising:

an implantable sensor configured to generate a signal that varies as a function of activity of a patient;

a processor configured to monitor a physiological signal of a patient based on the signal generated by the implantable sensor and predicts a gait freeze event based on the signal prior to onset of the gait freeze event; and

a gait cue module configured to provide a gait cue indicative of the predicted gait freeze event to the patient.

14. The system of claim 13, wherein the gait cue is a perceivable vibration.

15. The system of claim 13, wherein the gait cue is an electrical stimulus.

16. The system of claim 13, wherein the gait cue is a visual prompt.

17. The system of claim 13, wherein the gait cue is an audible stimulus.

18. The system of claim 13,

wherein the signal generated by the implantable sensor includes an electromyogram (EMG) signal, and

wherein the processor is configured to predict the gait freeze event based on irregular EMG activity in the EMG signal.

19. The system of claim 13,

wherein the signal generated by the implantable sensor includes an electroencephalogram (EEG) signal, and

wherein the processor is configured to predict the gait freeze event based on irregular EEG activity in the EEG signal.

20. A non-transitory computer-readable storage medium comprising instructions that cause a processor to:

monitor a physiological signal of a patient based on a signal generated by an implantable sensor within the patient;

predict a gait freeze event based on the signal prior to onset of the gait freeze event; and

issue instructions to a gait cue module to provide a gait cue indicative of the predicted gait freeze event to the patient.