Closed-Loop Estimation Method of Neurostimulation Strength-Duration Curve Using Fisher Information Optimization.

IF 4.4 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Seyed Mohammad Mahdi Alavi
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引用次数: 0

Abstract

Background: Strength-duration (SD) curve, rheobase and chronaxie parameters provide insights about neural activation dynamics and interdependence between pulse amplitude and duration, for diagnostics and therapeutic applications. The existing SD curve estimation methods are based on open-loop uniform and/or random pulse durations, which are chosen without feedback from neuronal data.

Objective: To develop a method for closed-loop estimation of the SD curve, where the pulse durations are adjusted iteratively using the neuronal data.

Method: In the proposed method, after the selection of each pulse duration through Fisher information matrix (FIM) optimization, the corresponding motor threshold (MT) is computed, the SD curve estimation is updated, and the process continues until satisfaction of a stopping rule based on the successive convergence of the SD curve parameters. The results are compared with various iterative uniform and random sampling techniques, where the SD curve estimation is updated after each sample.

Results: 250 simulation cases were run. The FIM method satisfied the stopping rule in 225 (90%) runs, and estimated the rheobase (chronaxie in parenthesis) with an average absolute relative error (ARE) of 1.57% (2.15%), with an average of 85 samples. At the FIM termination sample, methods with two and all random pulse durations, and uniform methods with descending, ascending and random orders led to 5.69% (20.09%), 2.22% (3.93%), 7.34% (40.90%), 3.10% (4.44%), and 2.05% (3.45%) AREs. In all 250 runs, the FIM method has chosen the minimum and maximum pulse durations as the optimal pulse durations for the SD curve estimation.

Conclusions: As proposed by the FIM method, the SD curve is identifiable by fitting to the data of the minimum and maximum pulse durations. However, the range of pulse duration should cover the vertical and horizontal parts of the SD curve. Also, iterative random or uniform samples from only the vertical or horizontal areas of the curve might not result in satisfactory estimation.

Significance: This paper provides insights about pulse durations selection for closed-loop and open-loop SD curve estimation.

利用费雪信息优化的神经刺激强度-持续时间曲线闭环估算法
背景:强度-持续时间(SD)曲线、流变基和时序参数有助于深入了解神经激活动态以及脉冲振幅和持续时间之间的相互依存关系,可用于诊断和治疗。现有的 SD 曲线估算方法基于开环统一和/或随机脉冲持续时间,这些脉冲持续时间是在没有神经元数据反馈的情况下选择的:目的:开发一种闭环自毁曲线估算方法,利用神经元数据迭代调整脉冲持续时间:方法:在所提出的方法中,通过费舍尔信息矩阵(FIM)优化选择每个脉冲持续时间后,计算相应的运动阈值(MT),更新 SD 曲线估计值,该过程一直持续到满足基于 SD 曲线参数连续收敛的停止规则为止。结果:运行了 250 个模拟案例。FIM 方法在 225 次(90%)运行中符合停止规则,并以平均绝对相对误差(ARE)1.57%(2.15%)估算了流变基(括号内为时差),平均采样 85 次。在 FIM 终止样本中,采用两种脉冲持续时间和所有随机脉冲持续时间的方法,以及采用降序、升序和随机顺序的统一方法,ARE 分别为 5.69% (20.09%)、2.22% (3.93%)、7.34% (40.90%)、3.10% (4.44%) 和 2.05% (3.45%)。在所有 250 次运行中,FIM 方法都选择了最小和最大脉冲持续时间作为 SD 曲线估计的最佳脉冲持续时间:正如 FIM 方法所提出的,通过拟合最小和最大脉冲持续时间的数据,可以识别 SD 曲线。然而,脉冲持续时间的范围应涵盖标清曲线的垂直和水平部分。此外,仅从曲线的垂直或水平区域迭代随机或均匀采样可能无法获得令人满意的估计结果:本文为闭环和开环标清曲线估算的脉冲持续时间选择提供了启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Biomedical Engineering
IEEE Transactions on Biomedical Engineering 工程技术-工程:生物医学
CiteScore
9.40
自引率
4.30%
发文量
880
审稿时长
2.5 months
期刊介绍: IEEE Transactions on Biomedical Engineering contains basic and applied papers dealing with biomedical engineering. Papers range from engineering development in methods and techniques with biomedical applications to experimental and clinical investigations with engineering contributions.
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