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

doi:10.1038/s41598-023-34801-9

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

Department of Neuroscience and Biomedical Engineering

Research output: Contribution to journal › Article › Scientific › peer-review

6 Citations (Scopus)

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Abstract

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 language	English
Article number	8225
Pages (from-to)	1-11
Number of pages	11
Journal	Scientific Reports
Volume	13
Issue number	1
DOIs	https://doi.org/10.1038/s41598-023-34801-9
Publication status	Published - Dec 2023
MoE publication type	A1 Journal article-refereed

Funding

This research has received funding from the Academy of Finland (Decision Nos. 255347, 265680, 294625, 306845, 348631, and 349985), the Finnish Cultural Foundation, Jane and Aatos Erkko Foundation, Erasmus Mundus SMART2 (No. 552042-EM-1-2014-1-FR-ERA MUNDUSEMA2), the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; grant number 140787/2014-3), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ConnectToBrain, grant agreement No. 810377), Personalized Health and Related Technologies (PHRT #2017-205) of the ETH Domain, Defitech Foundation and the Wyss Center for Bio and Neuroengineering.

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10.1038/s41598-023-34801-9Licence: CC BY

DELMEP - a deep learning algorithm for automated annotation of motor evoked potential latenciesFinal published version, 3.15 MBLicence: CC BY

Cite this

Milardovich, D., Souza, V. H., Zubarev, I., Tugin, S., Nieminen, J. O., Bigoni, C., Hummel, F. C., Korhonen, J. T., Aydogan, D. B., Lioumis, P., Taherinejad, N., Grasser, T., & Ilmoniemi, R. J. (2023). DELMEP : a deep learning algorithm for automated annotation of motor evoked potential latencies. Scientific Reports, 13(1), 1-11. Article 8225. https://doi.org/10.1038/s41598-023-34801-9

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title = "DELMEP : a deep learning algorithm for automated annotation of motor evoked potential latencies",

abstract = "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.",

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

note = "Funding Information: This research has received funding from the Academy of Finland (Decision Nos. 255347, 265680, 294625, 306845, 348631, and 349985), the Finnish Cultural Foundation, Jane and Aatos Erkko Foundation, Erasmus Mundus SMART2 (No. 552042-EM-1-2014-1-FR-ERA MUNDUSEMA2), the Conselho Nacional de Desenvolvimento Cient{\'i}fico e Tecnol{\'o}gico (CNPq; grant number 140787/2014-3), the European Research Council (ERC) under the European Union{\textquoteright}s Horizon 2020 research and innovation programme (ConnectToBrain, grant agreement No. 810377), Personalized Health and Related Technologies (PHRT \#2017-205) of the ETH Domain, Defitech Foundation and the Wyss Center for Bio and Neuroengineering. | openaire: EC/H2020/810377/EU//ConnectToBrain ",

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AU - Souza, Victor H.

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AU - Tugin, Sergei

AU - Nieminen, Jaakko O.

AU - Bigoni, Claudia

AU - Hummel, Friedhelm C.

AU - Korhonen, Juuso T.

AU - Aydogan, Dogu B.

AU - Lioumis, Pantelis

AU - Taherinejad, Nima

AU - Grasser, Tibor

AU - Ilmoniemi, Risto J.

N1 - Funding Information: This research has received funding from the Academy of Finland (Decision Nos. 255347, 265680, 294625, 306845, 348631, and 349985), the Finnish Cultural Foundation, Jane and Aatos Erkko Foundation, Erasmus Mundus SMART2 (No. 552042-EM-1-2014-1-FR-ERA MUNDUSEMA2), the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; grant number 140787/2014-3), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ConnectToBrain, grant agreement No. 810377), Personalized Health and Related Technologies (PHRT #2017-205) of the ETH Domain, Defitech Foundation and the Wyss Center for Bio and Neuroengineering. | openaire: EC/H2020/810377/EU//ConnectToBrain

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N2 - 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.

AB - 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.

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ER -

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

Abstract

Funding

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MOTO/Souza: Moving together- high resolution brain mapping technology for the motor pathways

Multi-locus transcranial magnetic stimulation

Multi-locus transcranial magnetic stimulation

Science-IT

Cite this

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

Abstract

Funding

Access to Document

Other files and links

Fingerprint

Projects

MOTO/Souza: Moving together- high resolution brain mapping technology for the motor pathways

Multi-locus transcranial magnetic stimulation

Multi-locus transcranial magnetic stimulation

Equipment

Science-IT

Cite this