Advanced therapy learning algorithm for spinal cord stimulation
Name
712966129-MIT.pdf
Description
Full printable version
Size
10.05 MB
Format
Adobe PDF
Checksum (MD5)
0f5efe41379c14c00990d651391702cc
Author(s)
Gaudreau Balderrama, Amanda Dawn
Advisor(s)
Collin M. Stultz and Eric Panken.
Alternative Title
Advanced therapy learning algorithm for SCS
Date Issued
2010
Publisher
Massachusetts Institute of Technology
Abstract
Spinal Cord Stimulation (SCS) is a technique used to treat chronic pain and has been shown to be an effective method of treatment, both financially and socioeconomically. Stimulating electrodes are surgically implanted into the epidural space, outside the dura, a protective sac filled with cerebral spinal fluid (CSF) surrounding the spinal cord. The thickness of the CSF changes according to body orientation, causing the distance between the stimulating electrodes and the spinal cord to vary. This phenomenon has been reported to cause painful or ineffective stimulation. In order to detect postural behavior and adjust SCS parameters accordingly, a tri-axial accelerometer based algorithm has been developed. The algorithm enables patients to adjust stimulation therapy parameters real-time, associates the patient indicated parameters with a vector, and stores them in a therapy library. Stimulation therapy parameters are then automatically selected by classifying incoming TA data according to the vectors in the therapy library, providing individualized, closed-loop stimulation therapy.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 85-87).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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