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NRV:一种用于评估周围神经电刺激策略的开放框架。

NRV: An open framework for evaluation of peripheral nerve electrical stimulation strategies.

作者信息

Couppey Thomas, Regnacq Louis, Giraud Roland, Romain Olivier, Bornat Yannick, Kölbl Florian

机构信息

ETIS CNRS UMR 8051, CY Cergy Paris University, ENSEA.

Univ. Bordeaux, Bordeaux INP, IMS CNRS UMR 5218, Aquitaine, Talence, France.

出版信息

bioRxiv. 2024 Jan 16:2024.01.15.575628. doi: 10.1101/2024.01.15.575628.

Abstract

Electrical stimulation of peripheral nerves has been used in various pathological contexts for rehabilitation purposes or to alleviate the symptoms of neuropathologies, thus improving the overall quality of life of patients. However, the development of novel therapeutic strategies is still a challenging issue requiring extensive experimental campaigns and technical development. To facilitate the design of new stimulation strategies, we provide a fully open source and self-contained software framework for the evaluation of peripheral nerve electrical stimulation. Our modeling approach, developed in the popular and well-established Python language, uses an object-oriented paradigm to map the physiological and electrical context. The framework is designed to facilitate multi-scale analysis, from single fiber stimulation to whole multifascicular nerves. It also allows the simulation of complex strategies such as multiple electrode combinations and waveforms ranging from conventional biphasic pulses to more complex modulated kHz stimuli. In addition, we provide automated support for stimulation strategy optimization and handle the computational backend transparently to the user. Our framework has been extensively tested and validated with several existing results in the literature.

摘要

外周神经电刺激已在各种病理情况下用于康复目的或缓解神经病变症状,从而改善患者的整体生活质量。然而,开发新的治疗策略仍然是一个具有挑战性的问题,需要广泛的实验研究和技术开发。为了便于设计新的刺激策略,我们提供了一个完全开源且自成体系的软件框架,用于评估外周神经电刺激。我们的建模方法是用广为人知且成熟的Python语言开发的,采用面向对象范式来映射生理和电学环境。该框架旨在促进多尺度分析,从单纤维刺激到整个多束神经。它还允许模拟复杂策略,如多种电极组合以及从传统双相脉冲到更复杂的千赫兹调制刺激等波形。此外,我们为刺激策略优化提供自动化支持,并为用户透明地处理计算后端。我们的框架已通过文献中的多个现有结果进行了广泛测试和验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8083/10827078/39b50afa41ba/nihpp-2024.01.15.575628v1-f0001.jpg

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