An Investigation Of Optimal Parameters For Electrical Nerve Stimulations With Circuit-Probability Theory

Electrical nerve stimulation is an important way of regulating the nervous system by generating action potentials in nerve fibers with an electric current.Due to the wide range of effects of electrical nerve stimulation,it has been used in the treatment of various diseases in recent years,which has attracted more and more researchers to conduct in-depth research on it.With the continuous deepening of the research on electrical nerve stimulation,the scientific community has found that the application of implantable neuromodulation is currently facing many difficulties,one of which is the energy consumption management of implantable neuromodulation devices.At present,various types of implantable neuromodulation devices generally use battery-powered solutions.Due to the limitation of battery lifetime,patients often need to replace the power source regularly,which increases the costs and risks of battery-replacement surgeries and severely limiting the commercial application of implantable medical devices.In conclusion,the study of energy optimization of electrical nerve stimulation is of great significance for prolonging the lifetime of implantable neural stimulators.Firstly,this thesis briefly introduces the neural model based on circuit-probability theory,which can numerically calculate the relationship between electrical stimulations(nervous system input signal)and neural responses(nervous system output signal)through the method of modeling nervous system.Taking advantage of the neural model based on circuit-probability theory,this thesis systematically studies the energy consumption of electrical stimulation under different neural response strength when the electrical stimulation waveform is monophasic negative square waveform,positivefirst biphasic square waveform and sine waveform.That is to say,for the neural response strength is low,the energy consumption trend of electrical stimulation increases with the pulse width;however,for the neural response strength is medium,the energy consumption curve first decreases and then increases with the pulse width;for the neural response strength is high,the energy consumption curve reduces monotonically with pulse width.For the different neural response strength,the changing trends of energy consumption are different.As the response strength increases,the pulse width of the current waveform of minimum energy consumption increases simultaneously.This conclusion can not only explain the reasons of the inconsistent and even contradictory views in previous research for the energy consumption comparison of different electrical stimulation waveforms,but also play an important guiding role in the design and commercial applications of implantable medical devices.Then,this thesis sets up corresponding animal experiments to verify the above theoretical research result.In the part of animal experiments,the overall experimental scheme and experimental system,the design,fabrication and testing procedures of the neural electrodes,the parameters setting of electrical stimulation waveform,the collection and processing method of experiment data and the final animal experiment data are introduced in turn.Finally,because of that the amount from animal experiment data is insufficient to prove the theoretical research conclusion on energy optimization of electrical nerve stimulation,a self-adaptive ensemble-based differential evolution algorithm is introduced in this thesis.With the self-adaptive ensemble-based differential evolution algorithm and animal experiment data,the parameters of the neural model based on circuit-probability theory are identified.With the model parameters,the neural model based on circuit-probability theory can calculate out the experiment data needed to be supplemented for verifying the theoretical research conclusion on energy optimization of electrical nerve stimulation.