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

IF 4.4 2区 医学 Q2 ENGINEERING, BIOMEDICAL IEEE Transactions on Biomedical Engineering Pub Date : 2024-08-27 DOI:10.1109/TBME.2024.3450789
Seyed Mohammad Mahdi Alavi
{"title":"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","authors":"Seyed Mohammad Mahdi Alavi","doi":"10.1109/TBME.2024.3450789","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>To develop a method for closed-loop estimation of the SD curve, where the pulse durations are adjusted iteratively using the neuronal data.</p><p><strong>Method: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Significance: </strong>This paper provides insights about pulse durations selection for closed-loop and open-loop SD curve estimation.</p>","PeriodicalId":13245,"journal":{"name":"IEEE Transactions on Biomedical Engineering","volume":"PP ","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Closed-Loop Estimation Method of Neurostimulation Strength-Duration Curve Using Fisher Information Optimization.\",\"authors\":\"Seyed Mohammad Mahdi Alavi\",\"doi\":\"10.1109/TBME.2024.3450789\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>To develop a method for closed-loop estimation of the SD curve, where the pulse durations are adjusted iteratively using the neuronal data.</p><p><strong>Method: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Significance: </strong>This paper provides insights about pulse durations selection for closed-loop and open-loop SD curve estimation.</p>\",\"PeriodicalId\":13245,\"journal\":{\"name\":\"IEEE Transactions on Biomedical Engineering\",\"volume\":\"PP \",\"pages\":\"\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Biomedical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1109/TBME.2024.3450789\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/TBME.2024.3450789","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

摘要

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。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Closed-Loop Estimation Method of Neurostimulation Strength-Duration Curve Using Fisher Information Optimization.

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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.
期刊最新文献
Table of Contents Front Cover IEEE Transactions on Biomedical Engineering Handling Editors Information IEEE Engineering in Medicine and Biology Society Information IEEE Transactions on Biomedical Engineering Information for Authors
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1