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Collin M. Stultz and Eric Panken. Gaudreau Balderrama, Amanda Dawn Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2011-05-09T15:12:43Z 2011-05-09T15:12:43Z 2010 2010 http://hdl.handle.net/1721.1/62639 712966129 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). 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. by Amanda Dawn Gaudreau Balderrama. M.Eng. 87 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Advanced therapy learning algorithm for spinal cord stimulation Advanced therapy learning algorithm for SCS Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="d5e72d3a-58ea-4c4d-bff1-b22f15e5e420"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Advanced therapy learning algorithm for spinal cord stimulation</Title> <Subtitle>Advanced therapy learning algorithm for SCS</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2010</PublicationDate> <Authors> <Author> <DisplayName>Gaudreau Balderrama, Amanda Dawn</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>