Influence of neck tissue conductivities on the phrenic nerve activation threshold during non-invasive electrical stimulation
Wegert, L.; Di Rienzo, L.; Codecasa, L.; Ziolkowski, M.; Hunold, A.; Kalla, T.; Lange, I.; Haueisen, J.
Show abstract
Phrenic nerve stimulation can be used as an artificial ventilation method to reduce the adverse effects of mechanical ventilation. Detailed computational models and electromagnetic simulations are used to determine appropriate stimulation parameters. Therefore, tissue parameters have to be selected, but they vary widely in the literature. Here, we evaluated the phrenic nerve activation threshold using minimum and maximum electrical conductivity values found in the literature of each modeled neck tissue type. To calculate the phrenic nerve activation threshold, an anatomical detailed finite element model of the neck and a biophysiological nerve model were used. Considerable changes in nerve activation thresholds were found for the following tissue conductivities (with decreasing effects): muscle, skin, soft tissue, subcutaneous fat, and nerve tissue. Changes in the nerve activation threshold due to changes in skin conductivity occurred due to the bridging effect, which is an unwanted and avoidable effect during stimulation. In conclusion, fat, muscle, nerve, and soft tissue require the most accurate tissue properties and geometric representation within the model.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Non-invasive stimulation with Temporal Interference: Optimization of the electric field deep in the brain with the use of a genetic algorithm 97%
- A Block-Capable and Module-Extendable 4-Channel Stimulator for Acute Neurophysiology 96%
- Frequency-dependent and capacitive tissue electrical properties in spinal cord stimulation models 96%
Similar papers in this journal
Similar papers in this journal
- Nonlinear Dispersive Cell Model for Microdosimetry of Nanosecond Pulsed Electric Fields 94%
- EEG-based diagnostics of the auditory system using cochlear implant electrodes as sensors 93%
- Automated methodology for optimal selection of the minimum electrode subset for accurate EEG source estimation based on Genetic Algorithm optimization 91%
Similar papers in this journal
- A low-cost protocol for reconditioning of deep-brain neural microelectrodes with material failure for electrophysiology recording 95%
- Biophysical modeling of the electric field magnitude and distribution induced by electrical stimulation with intracerebral electrodes 94%
- Model for Deformation of Cells from External Electric Fields at or Near Resonant Frequencies 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.