Integrative Neuroscience Group

Science and, Denmark

Integrative Neuroscience Group

Science and, Denmark
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Frahm K.S.,Integrative Neuroscience Group | Hennings K.,Nordic Neurostim | Vera-Portocarrero L.,Neuromodulation ResearchMedtronic IncMinneapolis | Wacnik P.W.,Neuromodulation ResearchMedtronic IncMinneapolis | Morch C.D.,Integrative Neuroscience Group
Neuromodulation | Year: 2016

Background: Peripheral nerve field stimulation (PNFS) is a potential treatment for chronic low-back pain. Pain relief using PNFS is dependent on activation of non-nociceptive Aβ-fibers. However, PNFS may also activate muscles, causing twitches and discomfort. In this study, we developed a mathematical model, to investigate the activation of sensory and motor nerves, as well as direct muscle fiber activation. Methods: The extracellular field was estimated using a finite element model based on the geometry of CT scanned lumbar vertebrae. The electrode was modeled as being implanted to a depth of 10-15 mm. Three implant directions were modeled; horizontally, vertically, and diagonally. Both single electrode and "between-lead" stimulation between contralateral electrodes were modeled. The extracellular field was combined with models of sensory Aβ-nerves, motor neurons and muscle fibers to estimate their activation thresholds. Results: The model showed that sensory Aβ fibers could be activated with thresholds down to 0.563 V, and the lowest threshold for motor nerve activation was 7.19 V using between-lead stimulation with the cathode located closest to the nerves. All thresholds for direct muscle activation were above 500 V. Conclusions: The results suggest that direct muscle activation does not occur during PNFS, and concomitant motor and sensory nerve fiber activation are only likely to occur when using between-lead configuration. Thus, it may be relevant to investigate the location of the innervation zone of the low-back muscles prior to electrode implantation to avoid muscle activation. © 2016 International Neuromodulation Society.


Rueterbories J.,Integrative Neuroscience Group | Spaich E.G.,Integrative Neuroscience Group | Andersen O.K.,Integrative Neuroscience Group
Medical Engineering and Physics | Year: 2014

Gait rehabilitation by Functional Electrical Stimulations (FESs) requires a reliable trigger signal to start the stimulations. This could be obtained by a simple switch under the heel or by means of an inertial sensor system. This study provides an algorithm to detect gait events in differential acceleration signals of the foot. The key feature of differential measurements is that they compensate the impact of gravity. The real time detection capability of a rule based algorithm in healthy and hemiparetic individuals was investigated. Detection accuracy and precision compared to signals from foot switches were evaluated. The algorithm detected curve features of the vectorial sum of radial and tangential accelerations and mapped those to discrete gait states. The results showed detection rates for healthy and hemiparetic gait ranging form 84.2% to 108.5%. The sensitivity was between 0.81 and 1, and the specificity between 0.85 and 1, depending on gait phase and group of subjects. The algorithm detected gait phase changes earlier than the reference. Differential acceleration signals combined with the proposed algorithm have the potential to be implemented in a future FES system. © 2013 IPEM.


PubMed | Integrative Neuroscience Group
Type: Evaluation Studies | Journal: Medical engineering & physics | Year: 2014

Gait rehabilitation by Functional Electrical Stimulations (FESs) requires a reliable trigger signal to start the stimulations. This could be obtained by a simple switch under the heel or by means of an inertial sensor system. This study provides an algorithm to detect gait events in differential acceleration signals of the foot. The key feature of differential measurements is that they compensate the impact of gravity. The real time detection capability of a rule based algorithm in healthy and hemiparetic individuals was investigated. Detection accuracy and precision compared to signals from foot switches were evaluated. The algorithm detected curve features of the vectorial sum of radial and tangential accelerations and mapped those to discrete gait states. The results showed detection rates for healthy and hemiparetic gait ranging form 84.2% to 108.5%. The sensitivity was between 0.81 and 1, and the specificity between 0.85 and 1, depending on gait phase and group of subjects. The algorithm detected gait phase changes earlier than the reference. Differential acceleration signals combined with the proposed algorithm have the potential to be implemented in a future FES system.

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