CN114692459A - Method for simulating peripheral nerve injury accurate array electrical stimulation - Google Patents

Method for simulating peripheral nerve injury accurate array electrical stimulation Download PDF

Info

Publication number
CN114692459A
CN114692459A CN202210326298.XA CN202210326298A CN114692459A CN 114692459 A CN114692459 A CN 114692459A CN 202210326298 A CN202210326298 A CN 202210326298A CN 114692459 A CN114692459 A CN 114692459A
Authority
CN
China
Prior art keywords
forearm
needle
electrode
nerve
electrical stimulation
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202210326298.XA
Other languages
Chinese (zh)
Other versions
CN114692459B (en
Inventor
宋西姊
褚晓蕾
明东
李奇
顾晓松
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tianjin University
Original Assignee
Tianjin University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tianjin University filed Critical Tianjin University
Priority to CN202210326298.XA priority Critical patent/CN114692459B/en
Publication of CN114692459A publication Critical patent/CN114692459A/en
Application granted granted Critical
Publication of CN114692459B publication Critical patent/CN114692459B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/02—Details
    • A61N1/04—Electrodes
    • A61N1/05—Electrodes for implantation or insertion into the body, e.g. heart electrode
    • A61N1/0551—Spinal or peripheral nerve electrodes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/3606—Implantable neurostimulators for stimulating central or peripheral nerve system adapted for a particular treatment
    • A61N1/36103—Neuro-rehabilitation; Repair or reorganisation of neural tissue, e.g. after stroke
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A61N1/36128—Control systems
    • A61N1/36146—Control systems specified by the stimulation parameters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Neurology (AREA)
  • Neurosurgery (AREA)
  • Physics & Mathematics (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Animal Behavior & Ethology (AREA)
  • Theoretical Computer Science (AREA)
  • Veterinary Medicine (AREA)
  • General Physics & Mathematics (AREA)
  • Geometry (AREA)
  • Medical Informatics (AREA)
  • Rehabilitation Therapy (AREA)
  • Epidemiology (AREA)
  • Surgery (AREA)
  • Primary Health Care (AREA)
  • Orthopedic Medicine & Surgery (AREA)
  • Computer Graphics (AREA)
  • Urology & Nephrology (AREA)
  • Software Systems (AREA)
  • Cardiology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Electrotherapy Devices (AREA)

Abstract

The invention relates to a method for simulating peripheral nerve injury accurate array electrical stimulation, which comprises the following steps: firstly, constructing a forearm geometric model; and secondly, constructing a forearm electrical stimulation model, and designing electrode array distribution through finite element calculation: needle electrodes are added at the two tail ends of the three-dimensional forearm geometric model, a forearm electrode array electrical stimulation geometric model is constructed, and the conductivity of the forearm geometric model is set according to the electrical property of each tissue of the forearm and the electrical property of the needle electrodes; applying current stimulation to the needle-shaped electrode, constructing a forearm electrical stimulation model, and solving Maxwell equations through finite element calculation to obtain the electric field distribution of nerves around forearm tissues; calculating the current density of the forearm nerve to evaluate the electric quantity of the stimulated nerve; and calculating a nerve fiber excitation function, and determining the distribution of the electrode array, including the number of the needle-shaped electrodes, the distance between the two needles and the included angle between the two needles.

Description

Method for simulating peripheral nerve injury accurate array electrical stimulation
Technical Field
The invention belongs to the field of peripheral nerve injury electrical stimulation methods, and particularly relates to a method for simulating peripheral nerve injury accurate array electrical stimulation.
Background
Approximately 42000 and 74000 patients with peripheral nerve injury in the United states annually have the peripheral nerve injury causing sensory and motor dysfunction, which seriously affects the quality of life of the patients. Electrical stimulation is currently one of the most effective therapeutic approaches to promote the recovery of peripheral nerve function. Koo, J research finds that electrical stimulation can promote the growth of damaged proximal axial buds after peripheral nerve injury, and further promote early functional recovery after peripheral nerve injury. Willand et al further discuss the mechanism to find: the electrical stimulation of 20HZ for 1 hour can promote depolarization of a cell body of a damaged proximal neuron, increase expression of nerve growth factors such as secondary BDNF and the like and accelerate axon regeneration.
In the process of treating peripheral nerve injury by electric stimulation, the electrode has important influence on the stimulation effect. Currently used stimulation electrodes are mainly divided into three categories: surface, subcutaneous and fully implantable. However, the implantable electrode has the characteristics of high surgical risk, easy displacement of the electrode, high implantation cost and the like, so that the electrode is not widely popularized and applied in China. Because the subcutaneous electrode has higher selectivity and targeting property, the subcutaneous electrode has low cost and is widely welcomed. Nowadays, acupuncture needles, also called electrical needles, are often used as subcutaneous electrodes. The electric needle has the defects of low electric quantity, limited range and the like. Accordingly, there has been studied a method in which a needle body is plated with a nano S12 coating layer and then connected to an electric acupuncture apparatus, and current is directed from a needle tip to a lesion site acting on a deep layer (a directional conductive acupuncture needle and a method for producing the same, CN 104415453A). However, this method has the following disadvantages: 1. the acupuncture needle needs to be specially modified, the acupuncture treatment cost is increased, and the popularization and the application are limited; 2. the acupuncture needle is punctured into an acupuncture point near nerve injury, and secondary nerve puncture is possible; 3. this approach only increases local electrical needle conductivity, while peripheral nerve injury often causes extensive nerve changes, and increasing only the depth of the electrical needle is difficult to meet the therapeutic needs. The matrix electrical stimulation formed by arranging the plurality of stimulation electrodes has certain advantages in improving the stimulation electricity quantity and the stimulation effect. There are studies on the stimulation of peripheral nerves by the microneedle array electrostimulation Method, which found that the microneedle array electrostimulation has high selectivity and stimulation intensity for the stimulation of peripheral nerves (Soltanzadeh R, Afshharipour E, Shafai C. interrogation of transient electric stimulation with micro electron array electronics positioning. int J Numer Method Biomed Eng.2020 Mar; 36(3): E3318.doi: 10.1002/cnm.8. Epub Feb 18.PMID: 32017406.). However, the research adopts an implantable microarray electrode, needs surgical implantation, and has the disadvantages of high cost, high risk and the like.
The therapeutic effect of the electric stimulation to treat the peripheral nerve injury is proved by various experiments, and the specific molecular biological mechanism of the electric stimulation is also widely concerned. The finite element is used as a mathematical and physical model with wide application, and can effectively supplement from an electrical theory and verify the deficiency of a biological mechanism. (Gordon, T., Electrical simulation to Enhance Axon Regeneration After Peripheral inner sources in Animal Models and human. Neurotheropeptics, 2016.13(2): p.295-310.); (Fu, T, et al, electric Muscle Stimulation accesses Functional Recovery After neural input. neural science,2020.426: p.179-188.); (Park, S., et al, Effects of repaired 20-Hz Electric simulation on Functional Recovery from Peripheral Nerve Repair,2019.33(9): p.775-784.) (Bunn, S.J., A.Lai and J.Li, DC Electric Fields induced compatibility and Enhanced simulation in Schwann cells culture, Ann biological Eng,2019.) (Fei, J.et al, Electric sources produced by Peripheral Nerve Repair and Repair, Fei, J.E., Cell Repair and Repair, and production of plasma Repair and Repair in 354. n. the results of the simulation and Repair of the Functional Repair, and the results of the Functional Repair, and Repair of the Functional Repair, 3. n. the Functional Repair, N.E. and Repair, and Repair of the Functional Repair, N.E. and Repair, N.E. the Functional Repair, and Repair of the Functional Repair, and Repair, 3. the Functional Repair, III, and Repair; (Elder, C.W.and P.B.Yoo, A fine element modifying student of a peripheral negative biological neutral simulation in the human lower leg. Med Eng Phys,2018.53: p.32-38.); frahm, K.S., et al, New Fiber Activation Dual Peripheral New Field Stimulation, Import of Electrode organization and Estimation of Area of Paresthesia.Neuromechanical, 2016.19(3): p.311-8.
Disclosure of Invention
The invention provides a method for simulating accurate array electrical stimulation of peripheral nerve injury, which utilizes COMSOL Multiphysics to establish a finite element model of forearm nerve injury, and determines electrode array distribution including electric needle distance, electric needle included angle and electric needle number according to a conductivity equation. The technical scheme is as follows:
a method of simulating accurate array electrical stimulation of peripheral nerve injury comprising the steps of:
the first step is to construct a forearm geometric model:
constructing a forearm three-dimensional geometric model through a GUI geometric modeling module of COMSOL Multiphysics based on forearm nuclear magnetic data;
secondly, constructing a forearm electrical stimulation model, and designing electrode array distribution through finite element calculation
Needle electrodes are added at the two tail ends of the three-dimensional forearm geometric model, a forearm electrode array electrical stimulation geometric model is constructed, and the conductivity of the forearm geometric model is set according to the electrical property of each tissue of the forearm and the electrical property of the needle electrodes;
applying current stimulation to the needle-shaped electrode, constructing a forearm electrical stimulation model, and solving Maxwell equations through finite element calculation to obtain the electric field distribution of nerves around forearm tissues; calculating forearm nerve current density
Figure BDA0003573567500000021
For evaluating stimulated nerve charge; calculating a nerve fiber excitation function uxx，Determining the distribution of the electrode array according to the following steps, wherein the distribution comprises the number of the needle-shaped electrodes, the distance between two needles and the included angle between the two needles:
(1) determining the distance between two needles of the needle-shaped electrode:
sequentially constructing forearms with different needle-shaped electrode two-needle distancesAn electrode array electrical stimulation model is used for calculating the forearm nerve current density of the needle electrode under different distances
Figure BDA0003573567500000022
And nerve fiber excitation function uxx(ii) a Comparing the current densities of different electrode distances with the nerve fiber excitation function result, and selecting the needle electrode distance with the maximum current density and excitation function as the optimal needle electrode distance;
(2) determination of the number of needle electrodes:
constructing forearm electrode array electrical stimulation models with different needle electrode numbers by combining the average length of the human forearm anatomy and the human body bearing capacity; calculating the current density of forearm nerve under the number of the needle electrodes
Figure BDA0003573567500000031
And nerve fiber excitation function uxx(ii) a Comparing the current density of different electrode numbers with the nerve fiber excitation function result, and selecting the number of the needle electrodes with the current density and the maximum excitation function as the optimal number of the needle electrodes;
(3) determining the relative included angle of two needles of the needle-shaped electrode:
constructing a forearm electrode array electrical stimulation model of different needle electrode relative angles, and calculating forearm nerve current density of the needle electrodes under different angles
Figure BDA0003573567500000033
And nerve fiber excitation function uxxAnd comparing the current density of different electrode angles with the nerve fiber excitation function result, and selecting the relative included angle of the needle electrode with the maximum excitation function as the optimal needle electrode angle.
According to the invention, by providing a simulation method, on the basis of establishing a forearm geometric model and a forearm electrical stimulation model, a needle stimulation electrode distribution strategy is optimized, and simulation basis and guidance can be provided for a treatment scheme of simulating peripheral nerve injury accurate array electrical stimulation.
Drawings
The following drawings depict selected embodiments of the present invention, all by way of example and not by way of exhaustive or limiting example, and are presented in the figures of the accompanying drawings:
FIG. 1 is a schematic view of the overall scheme
FIG. 2 is a distribution diagram of an electrode array
FIG. 3 is a detail view of a forearm finite element model geometry model with the needle electrode perpendicular to the forearm geometry model.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
The method for accurately electrically stimulating peripheral nerve injury based on an electrode array of the present invention is described below with reference to the accompanying drawings and embodiments, which are intended to be described as embodiments of the present invention, and not to be the only form in which the method can be manufactured or utilized, and other embodiments capable of achieving the same function are also included in the scope of the present invention.
Figure BDA0003573567500000032
Table one: forearm geometric model size and conductivity parameters
The novel method for accurately electrically stimulating the peripheral nerve injury based on the electrode array can be roughly divided into the following three steps, and fig. 1 is a schematic diagram of the overall scheme:
1. constructing a forearm geometric model:
according to the actual anatomical structure of the forearm of a human body, an elliptic cylindrical forearm three-dimensional geometric model is constructed through a GUI geometric modeling module carried by COMSOL Multiphysics based on forearm nuclear magnetic data. Forearm geometric models include skin, fat, blood vessels, muscle, bone and nerves, see figure 3 for details, and table one for specific dimensions.
2. Constructing a forearm electrode array electrical stimulation model, and designing electrode array distribution based on an optimization method through finite element calculation:
on the basis of the forearm three-dimensional geometric model constructed in the step 1, needle electrodes are added at two tail ends of the three-dimensional forearm geometric model, and the needle pitch is 12cm, so that a forearm electrode array electrical stimulation geometric model is constructed, as shown in fig. 3. The geometrical model conductivity was set according to the electrical properties of the respective tissues of the forearm and the electrical properties of the needle electrode, and the set conductivity values and permittivity were as shown in Table I (Gabriel, S, RW Lau, C Gabriel. the dielectric properties of biological properties: III. parametric models for the dielectric spectra of properties [ J ]. Phys Med Biol,1996,41 (11): 2271-93).
And applying current stimulation to the needle electrode, wherein the current value is 1mA, constructing a forearm electrical stimulation model, and solving the following Maxwell equation by a COMSOL Multiphysics finite element calculation method to obtain the electric field distribution of the nerves around the forearm tissue.
Figure BDA0003573567500000041
Wherein,
Figure BDA0003573567500000042
is a differential operator, sigma is the conductivity distribution of the forearm, omega is the area represented by the forearm model, j is the current density applied to the needle electrode, u is the electric field distribution of the peripheral nerves of the forearm tissue obtained by calculation,
Figure BDA0003573567500000043
is referred to as the partial derivative. And then calculating the forearm nerve current density according to the equation (2-3)
Figure BDA0003573567500000044
The current density refers to the density at which electric charges flow, i.e., the amount of current per unit cross-sectional area. The current density is usually used for measuring the magnitude of the electric quantity and is directly related to the therapeutic effect of the electric stimulation, and the electric quantity of the stimulated nerve is evaluated by using the index.
Figure BDA0003573567500000045
Figure BDA0003573567500000046
Wherein,
Figure BDA0003573567500000047
is the electric field strength.
uxxThe degree of influence of extra-membrane current on intra-membrane current is characterized for the second derivative of the intra-tissue potential along the nerve fiber axon, i.e. the nerve fiber excitation function. The nerve fiber excitation function was calculated according to formula (4) to evaluate the nerve fiber excitability. When u isxxWhen the transmembrane potential is positive, the neuron depolarizes and becomes excited.
Figure BDA0003573567500000051
According to the following steps, the electrode array distribution is determined, including the number of needle-shaped electrodes, the distance between two needles and the included angle between two needles.
(1) Determining the distance between two needles of the needle-shaped electrode:
and sequentially constructing a forearm electrode array electrical stimulation model with the needle electrode distance of 1-12cm by taking the needle electrode distance of 1cm as a step length. Calculating the current density of the forearm nerve at different distances of the needle electrode by solving the equations (1-4) respectively
Figure BDA0003573567500000052
And nerve fiber excitation function uxx. According to the optimization principle, comparing the current densities of different electrode distances with the nerve fiber excitation function result, and selecting the needle electrode distance with the maximum excitation function as the optimal needle electrode distance. In this example, the distance between the two needles of the needle electrode was determined to be 5 cm.
(2) Determination of the number of needle electrodes:
and sequentially constructing a forearm electrode array electrical stimulation model with the number of the needle electrodes of 2-8 by taking the number of the needle electrodes as a step length and combining the average anatomical length of the forearm of the human body and the bearing capacity of the human body. Calculating the current density of the forearm nerve under the number of the needle electrodes by solving the equation (1-4) respectively
Figure BDA0003573567500000053
And nerve fiber excitation function uxx. According to the optimization principle, comparing the current densities of different electrode numbers with the results of the nerve fiber excitation function, and selecting the number of the needle electrodes with the current densities and the maximum excitation function as the optimal number of the needle electrodes. In this example, the number of the needle electrodes was determined to be 6.
(3) Determining the relative included angle of two needles of the needle-shaped electrode:
and sequentially constructing a forearm electrode array electrical stimulation model of the needle electrodes at 0-180 degrees by taking the relative angle of the needle electrodes at 15 degrees as a step length. Calculating the current density of the forearm nerve at different angles of the needle electrode by solving the equations (1-4) respectively
Figure BDA0003573567500000054
And nerve fiber excitation function uxx. According to the optimization principle, comparing the current densities of different electrode angles with the nerve fiber excitation function result, and selecting the relative included angle of the needle electrode with the maximum excitation function as the optimal needle electrode angle. In this embodiment, the relative angle between the two needles of the needle electrode is determined to be 0 degree.
The present invention and the embodiments thereof have been described in an illustrative and non-restrictive manner, and it should be understood that the structural forms or embodiments similar to the present invention may be devised without departing from the spirit and scope of the present invention by those skilled in the art.

Claims (1)

1. A method of simulating accurate array electrical stimulation of peripheral nerve injury comprising the steps of:
the first step is to construct a forearm geometric model:
based on the forearm nuclear magnetic data, a forearm three-dimensional geometric model was constructed by the GUI geometric modeling module of COMSOL Multiphysics.
Secondly, constructing a forearm electrical stimulation model, and designing electrode array distribution through finite element calculation
Needle electrodes are added at the two tail ends of the three-dimensional forearm geometric model, a forearm electrode array electrical stimulation geometric model is constructed, and the conductivity of the forearm geometric model is set according to the electrical property of each tissue of the forearm and the electrical property of the needle electrodes;
applying current stimulation to the needle-shaped electrode, constructing a forearm electrical stimulation model, and solving Maxwell equations through finite element calculation to obtain the electric field distribution of nerves around forearm tissues; calculating forearm nerve current density
Figure FDA0003573567490000011
For evaluating stimulated nerve charge; calculating a nerve fiber excitation function uxxDetermining the distribution of the electrode array according to the following steps, including the number of the needle-shaped electrodes, the distance between the two needles and the included angle between the two needles:
(1) determining the distance between two needles of the needle-shaped electrode:
sequentially constructing forearm electrode array electrical stimulation models of different needle electrode two-needle distances, and calculating forearm nerve current density of the needle electrode at different distances
Figure FDA0003573567490000012
And nerve fiber excitation function uxx(ii) a Comparing the current densities of different electrode distances with the nerve fiber excitation function result, and selecting the needle electrode distance with the current density and the maximum excitation function as the optimal needle electrode distance;
(2) determination of the number of needle electrodes:
constructing forearm electrode array electrical stimulation models with different needle electrode numbers by combining the average length of the human forearm anatomy and the human body bearing capacity; calculating the current density of forearm nerve under the number of the needle electrodes
Figure FDA0003573567490000013
And nerve fiber excitation function uxx(ii) a Comparing the current density of different electrode numbers with the nerve fiber excitation function result, and selecting the number of the needle electrodes with the current density and the maximum excitation function as the optimal number of the needle electrodes;
(3) determining the relative included angle of two needles of the needle-shaped electrode:
constructing a forearm electrode array electrical stimulation model of different needle electrode relative angles, and calculating forearm nerve current density of the needle electrodes under different angles
Figure FDA0003573567490000014
And nerve fiber excitation function uxxThe current density and nerve fibers were compared for different electrode angles.
CN202210326298.XA 2022-03-30 2022-03-30 Method for simulating peripheral nerve injury accurate array electrical stimulation Active CN114692459B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210326298.XA CN114692459B (en) 2022-03-30 2022-03-30 Method for simulating peripheral nerve injury accurate array electrical stimulation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210326298.XA CN114692459B (en) 2022-03-30 2022-03-30 Method for simulating peripheral nerve injury accurate array electrical stimulation

Publications (2)

Publication Number Publication Date
CN114692459A true CN114692459A (en) 2022-07-01
CN114692459B CN114692459B (en) 2024-07-05

Family

ID=82140198

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210326298.XA Active CN114692459B (en) 2022-03-30 2022-03-30 Method for simulating peripheral nerve injury accurate array electrical stimulation

Country Status (1)

Country Link
CN (1) CN114692459B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116531662A (en) * 2023-05-08 2023-08-04 天津大学 Peripheral nerve injury noninvasive targeting electric stimulation device based on coherent electricity
CN116702534A (en) * 2023-03-31 2023-09-05 天津大学 Spinal cord injury accurate electric stimulation simulation method and electric stimulation device based on coherent electricity

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106345056A (en) * 2016-09-21 2017-01-25 天津大学 Deep brain stimulation electrode array optimizing system based on machine learning

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106345056A (en) * 2016-09-21 2017-01-25 天津大学 Deep brain stimulation electrode array optimizing system based on machine learning

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
张锦秋;王海博;王晓伟;安茂忠;: "有限单元法模拟微小零件电流密度分布", 电镀与环保, no. 02, 30 March 2015 (2015-03-30) *
徐琦;胡能宇;周厚纶;王熠钊;: "采用表面阵列电极的神经肌肉电刺激系统设计与优化", 中国生物医学工程学报, no. 02, 20 April 2013 (2013-04-20) *
黄涛;徐琦;何际平;周厚纶;王熠钊;: "采用表面阵列电极的人体前臂电刺激仿真研究", 中国生物医学工程学报, no. 03, 20 June 2012 (2012-06-20), pages 99 - 104 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116702534A (en) * 2023-03-31 2023-09-05 天津大学 Spinal cord injury accurate electric stimulation simulation method and electric stimulation device based on coherent electricity
CN116702534B (en) * 2023-03-31 2024-07-09 天津大学 Spinal cord injury accurate electric stimulation simulation method and electric stimulation device based on coherent electricity
CN116531662A (en) * 2023-05-08 2023-08-04 天津大学 Peripheral nerve injury noninvasive targeting electric stimulation device based on coherent electricity

Also Published As

Publication number Publication date
CN114692459B (en) 2024-07-05

Similar Documents

Publication Publication Date Title
CN106215325B (en) Waveform shape optimized for energy efficiency for treating neurological disorders
CN114692459A (en) Method for simulating peripheral nerve injury accurate array electrical stimulation
Wei et al. Current density distributions, field distributions and impedance analysis of segmented deep brain stimulation electrodes
Wongsarnpigoon et al. Energy-efficient waveform shapes for neural stimulation revealed with a genetic algorithm
US8944985B2 (en) Deep brain stimulation implant with microcoil array
JP5653918B2 (en) Apparatus and method for optimized stimulation of neural targets
Golestanirad et al. Analysis of fractal electrodes for efficient neural stimulation
US9526890B2 (en) Electrode designs for efficient neural stimulation
JP2012510871A (en) Implantable nerve stimulation device with reduced pocket stimulation
EP3448503A1 (en) Alternative electrode configurations for reduced power consumption
CN101856302B (en) Asychronous multi-channel stimulation electric acupuncture needle and preparation method thereof
Lee et al. An efficient noninvasive neuromodulation modality for overactive bladder using time interfering current method
Gomez-Tames et al. A simulation study on the dominance of the Tissues' conductivity in the muscle recruitment
CN116702534B (en) Spinal cord injury accurate electric stimulation simulation method and electric stimulation device based on coherent electricity
Lovell et al. Simulation of parallel current injection for use in a vision prosthesis
Verma et al. Augmented transcutaneous stimulation using an injectable electrode: a computational study
CN116884618A (en) Human body bladder focusing electric stimulation method based on finite element modeling and simulated annealing optimization algorithm
Pavselj et al. A numerical model of permeabilized skin with local transport regions
Abramian et al. Computational model of electrical stimulation of a retinal ganglion cell with hexagonally arranged electrodes
CN104984479B (en) Suppression system and suppressing method that electronics clashes to specific cell
Agotici et al. Computational study on spatially distributed sequential stimulation for fatigue resistant neuromuscular electrical stimulation
Xu et al. Epidural stimulation of rat spinal cord at lumbosacral segment using a surface electrode: a computer simulation study
Rattay Research in electrical stimulation fundamentals.
Salkım OPTIMAL LOCATION OF ACTION POTENTIAL GENERATION BASED ON ACTIVATION FUNCTION USING COMPUTATIONAL MODELLING
Iseri et al. Characterization of Induced Current Density During Transcorneal Electrical Stimulation to Promote Neuroprotection in the Degenerating Retina

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant