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Optimization of lead design and electrode configuration in Deep Brain Stimulation
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2016 (English)In: International Journal On Advances in Life Sciences, ISSN 1942-2660, E-ISSN 1942-2660, Vol. 8, p. 76-86Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2016. Vol. 8, p. 76-86
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-305224OAI: oai:DiVA.org:uu-305224DiVA, id: diva2:1034761
Available from: 2016-06-30 Created: 2016-10-13 Last updated: 2018-03-29Bibliographically approved
In thesis
1. Model-based optimization for individualized deep brain stimulation
Open this publication in new window or tab >>Model-based optimization for individualized deep brain stimulation
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Deep Brain Stimulation (DBS) is an established therapy that is predominantly  utilized in treating the symptoms of neurodegenerative diseases such as Parkinson's Disease and Essential Tremor, crippling diseases like Chronic Pain and Epilepsy, and psychiatric diseases such as Schizophrenia and Depression. Due to its invasive nature, DBS is considered as a last resort therapy.DBS is performed by transmitting electric pulses through an electrode implanted in the brain of the patient.

The stimulation is driven by a battery-powered Implanted Pulse Generator. The brain is a very delicate and complex organ and, therefore, accurate positioning the electrode is vital. To achieve a satisfactory therapeutical result, the stimulation targets a certain predefined brain structure that depends on the disease.

The effect of DBS depends on the individual, the chosen stimulating contact(s), and the pulse parameters, i.e. amplitude, frequency, width, and shape. Tuning these parameters to the best effect is currently done by a lengthy trial-and-error process. Insufficient stimulation does not properly alleviate the symptoms of the disease, while overstimulation or stimulation off target is prone to side effects.

This work envisions assisting physicians in DBS therapy by utilizing model-based estimation and optimization, maximizing stimulation of the target and minimizing stimulation in potentially problematic areas of the brain. This work focuses on amplitude and contact selection. Because of inter-patient differences, individualized models based on clinical imaging have to be created. Alternatively, semi-individualized models can be designed using atlases that save time but potentially introduce inaccuracies. Other optimization  applications to DBS are proposed in the thesis, e.g. fault alleviation and electrode design.

Electrical properties of the brain can change over time and alter the stimulation spread. A system identification approach has been proposed to quantify these changes.

The main aim of DBS is to alleviate the symptoms of the disease and quantifying symptoms is important. The ultimate vision of this work is to design a closed-loop system that can deliver optimal stimulation to the brain while automatically adapting to changes in the brain and the severity of symptoms.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 68
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1659
Keyword
Neuromodulation, Deep Brain Stimulation, Inverse Problems, Optimization, Finite Element Methods
National Category
Control Engineering
Research subject
Electrical Engineering with specialization in Automatic Control
Identifiers
urn:nbn:se:uu:diva-347353 (URN)978-91-513-0306-2 (ISBN)
Public defence
2018-05-25, ITC 2446 (Polacksbacken), Lägerhyddsvägen 2, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2018-05-03 Created: 2018-03-29 Last updated: 2018-05-04

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