uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Online tissue conductivity estimation 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, Division of Systems and Control.
(English)In: Article in journal (Refereed) Submitted
Abstract [en]

Deep Brain Stimulation (DBS) is an established therapy that consists of sending electrical pulses to the brain via a chronically implanted electrode. DBS is used to alleviate symptoms of neurological medical conditions such as Parkinson’s Disease and Essential Tremor. However, the stimulation effect is patient-specific and hard to predict. In addition, the brain tissue around the lead changes its electrical properties over time thus influencing the therapeutical effect. This paper proposes an approach to online conductivity estimation by sending a specially designed electrical probing signal into the brain from one contact of the electrode and measuring the response on another one. A conductivity estimate is then obtained from the parameters of an estimated linear time-invariant model. Both voltage-controlled and current-controlled DBS systems are treated. Continuous Least Squares and Laguerre domain identification are employed to estimate the involved models. Results suggest that a smooth Gaussian-shaped signal is sufficient to identify the model in a noise-free situation, but the resulting excitation might be not enough for accurate estimation in the face of disturbances, e.g. local field potential due to neuron firings. Better excitation is produced by input signals with designed Laguerre spectra. Due to the infeasibility of assigning electrical properties to the surroundings of the lead in vivo, synthetic data from an individualized high-fidelity mathematical model of DBS are used instead. The latter model is validated against clinically measured impedance values.

National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-347346OAI: oai:DiVA.org:uu-347346DiVA, id: diva2:1194186
Available from: 2018-03-29 Created: 2018-03-29 Last updated: 2018-03-29
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
Keywords
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: 2018-10-08

Open Access in DiVA

No full text in DiVA

Search in DiVA

By author/editor
Cubo, RubenMedvedev, Alexander
By organisation
Division of Systems and Control
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 31 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf