DELMEP : a deep learning algorithm for automated annotation of motor evoked potential latencies

Diego Milardovich*, Victor H. Souza, Ivan Zubarev, Sergei Tugin, Jaakko O. Nieminen, Claudia Bigoni, Friedhelm C. Hummel, Juuso T. Korhonen, Dogu B. Aydogan, Pantelis Lioumis, Nima Taherinejad, Tibor Grasser, Risto J. Ilmoniemi

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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.

Original languageEnglish
Article number8225
Pages (from-to)1-11
Number of pages11
JournalScientific Reports
Issue number1
Publication statusPublished - Dec 2023
MoE publication typeA1 Journal article-refereed


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