METHOD AND APPARATUS FOR CONTINUOUS MEASUREMENT OF MOTOR SYMPTOMS IN PARKINSON'S DISEASE AND ESSENTIAL TREMOR WITH WEARABLE SENSORS

• US 20100030119A1
• Filed: 07/28/2009
• Published: 02/04/2010
• Est. Priority Date: 07/29/2008
• Status: Active Grant

• Current Assignee: APDM, Inc., Oregon State University (Oregon University System), Wearables IP Holdings LLC
• Original Assignee: APDM, Inc.
• Inventors: Riobo Aboy, Pedro Mateo, McNames, James, Greenberg, Andrew
• Granted Patent: US 9,301,712 B2
• Time in Patent Office: Days
• US Class Current: 600/595
• CPC Class Codes: A61B 5/1101 Detecting tremor
• CPC Class Codes: A61B 5/4082 Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette

Abstract

Disclosed embodiments include an apparatus for generating a plurality of movement impairment indices from one or more kinematic signals to characterize movement disorders. Additionally we disclose methods for generating a plurality of movement impairment indices from one or more kinematic signals obtained from one or more kinematic sensors, said methods implemented in a digital computer with one or more processors in order to characterize movement disorders based on spectral analysis, regularity metrics, and time-frequency analsysis.

Claims

1. A method for generating a plurality of movement impairment indices from one or more kinematic signals obtained from one or more kinematic sensors, said method implemented in a digital computer with one or more processors in order to characterize movement disorders, said method comprising:

(a) collecting said kinematic signals from said kinematic sensors;

(b) processing said kinematic signals to reject artifacts and noise resulting in enhanced kinematic signals;

(c) finding a measure of power spectral density of said enhanced kinematic signals;

(e) calculating a plurality of impairment indices from said measure of power spectral density.

2. The method of claim 1, further comprising detrending said enhanced kinematic signals prior to finding a measure of power spectral density of said enhanced kinematic signals.

3. The method of claim 1, further comprising combining said enhanced kinematic signals to integrate the information contained in different axis and said kinematic sensors.

4. The method of claim 1, wherein calculating a plurality of impairment indices from said measure of power spectral density involves computing a plurality of bandlimited signal powers corresponding to specific impairment conditions.

5. The method of claim 1, further comprising postprocessing said plurality of impairment indices from said measure of power spectral density to generate a plurality of smoother impairment indices.

6. The method of claim 1, wherein calculating a plurality of impairment indices from said measure of power spectral density involves computing a plurality of regularity metrics.

7. The method of claim 1, wherein finding a measure of power spectral density of said enhanced kinematic signals includes generating a time-frequency analysis plot.

8. The method of claim 1, wherein finding a measure of power spectral density of said enhanced kinematic signals involves using a power spectral estimation method chosen from the group consisting of periodogram, modified periodogram, Barletts'"'"'s method, Welch'"'"'s method, Blackman-Tukey'"'"'s method, autoregressive spectrum estimation, moving average spectrum estimation, autoregressive moving average spectrum estimation, and adaptive spectrum estimation.

9. The method of claim 1, wherein kinematic sensors include accelerometers, gyroscopes, and magnetometers.

10. The method of claim 6, wherein said regularity metrics are chosen from the group consisting of spectral flatness, eigenvalue spread, Lempel-Ziv complexity, Approximate Entropy, Sample Entropy, Multi-Scale entropy, nonlinear metrics, and complexity metrics.

11. The method of claim 7, wherein said time-frequency analysis plot is a spectrogram.

12. The method of claim 7, wherein said time-frequency analysis plot is a scalogram.

13. A method for analyzing and visualizing kinematic data to characterize movement disorders, comprising:

(a) collecting kinematic signals from a plurality of kinematic sensors; and

(b) calculating and displaying a time-frequency plot of said plurality of kinematic signal;

whereby said time-frequency plot can be used to determine impairment conditions such as tremor, dyskinesia, and bradykinesia from said kinematic signals.

14. The method of claim 13, wherein said time-frequency analysis plot is a spectrogram.

15. The method of claim 13, wherein said time-frequency analysis plot is a scalogram.

16. An apparatus for generating a plurality of movement impairment indices from one or more kinematic signals to characterize movement disorders, comprising:

(a) one or more kinematic sensors to collect said kinematic signals;

(b) means for processing said kinematic signals to reject artifacts and noise resulting in enhanced kinematic signals;

(c) means finding a measure of power spectral density of said enhanced kinematic signals;

(e) means for calculating a plurality of impairment indices from said measure of power spectral density; and

(e) means for displaying said plurality of impairment indices.