Institute for Microelectronics, Technische Universität Wien, Gußhausstraße 27-29/E360, 1040, Vienna, Austria.
Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.
Sci Rep. 2023 May 22;13(1):8225. doi: 10.1038/s41598-023-34801-9.
The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characterization of thousands of MEPs. Given the difficulty of developing reliable and accurate algorithms, currently the assessment of MEPs is performed with visual inspection and manual annotation by a medical expert; making it a time-consuming, inaccurate, and error-prone process. In this study, we developed DELMEP, a deep learning-based algorithm to automate the estimation of MEP latency. Our algorithm resulted in a mean absolute error of about 0.5 ms and an accuracy that was practically independent of the MEP amplitude. The low computational cost of the DELMEP algorithm allows employing it in on-the-fly characterization of MEPs for brain-state-dependent and closed-loop brain stimulation protocols. Moreover, its learning ability makes it a particularly promising option for artificial-intelligence-based personalized clinical applications.
经颅磁刺激（TMS）产生的运动诱发电位（MEPs）分析在研究和临床医疗实践中至关重要。MEPs 的特征在于潜伏期，单个患者的治疗可能需要对数千个 MEPs 进行特征描述。鉴于开发可靠且准确算法的难度，目前 MEPs 的评估是通过医学专家进行视觉检查和手动注释来完成的；这是一个耗时、不准确且容易出错的过程。在这项研究中，我们开发了基于深度学习的 DELMEP 算法，以实现 MEPs 潜伏期的自动估计。我们的算法导致平均绝对误差约为 0.5 毫秒，并且准确性几乎与 MEP 幅度无关。DELMEP 算法的低计算成本允许在脑状态相关和闭环脑刺激协议中对 MEPs 进行实时特征描述。此外，其学习能力使其成为基于人工智能的个性化临床应用的一个特别有前途的选择。
