Naslov
Development of a New Algorithm for Automatic Latency Estimation of Motor Evoked Potentials in TMS studies

Autori
Rogić Vidaković, Maja ; Šoda, Joško ; Jerković, Ana ; Vujović, Igor ; Lorincz, Josip ; Zulim, Ivana ; Đogaš, Zoran

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), stručni

Izvornik
Abstracts from the 11th International Symposium on nTMS in Neurosurgery and Neuromodulation / - , 2019, 9-10

Skup
11th International Symposium on nTMS in Neurosurgery and Neuromodulation

Mjesto i datum
Berlin, Njemačka, 08.11.2019. - 09.11.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Recenziran

Ključne riječi
MEP ; latency ; TMS ; algorithm

Sažetak
Background: An evaluation of motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) over the motor cortex (M1) provides a quantification of cortico-spinal excitability at the time of stimulation. Specific parameters of MEP responses can be investigated such as: latency, peak-to-peak amplitude (Vpp), duration (iDur), terminal-excluded duration (eDur), number of turns (NT), number of phases (NP), area under the curve (AUC), thickness and size index [1]. Perhaps latency and peak-to-peak amplitude are one of the mostly used measures in the research and clinical settings. Currently, there are various (free) tools available for estimation of above-mentioned MEP measures in so called on-line and off-line mode. However, the amplitude of MEP response can vary due to fluctuations in neural excitability at the cortical and spinal levels, therefore an estimation of the MEP latency degrades with the lower amplitudes. Study objective: The aim of the present study was to present a new algorithm for MEP latency estimation which improves the accuracy of latency estimation and to compare it with: the standard method (available algorithms) [2, 3, 4, 5, 6] and manual estimation performed by two independent examiners. Methods: The proposed algorithm was efficiently applied on a total of 700 signals (MEPs) recorded from two hand muscles, acquired in the previous study investigating short- afferent latency inhibition in ten healthy subjects. It was found that available algorithms lack precision in latency estimation for the MEPs with the Vpp lower than 100 µV, where signal- to-noise (S/N) ratio is lower than with “strong” MEPs, therefore, the following amplitude ranges: Vpp< 100 µV, 100 µV < Vpp< 300 µV and Vpp> 300 µV were used for comparison of our new algorithm, standard method and manual estimation. Results: The proposed algorithm for MEP latency estimation proved to be successful as the standard methods and accurate enough within a manual assessment, with significantly better achievements of the proposed algorithm compared to the standard method in the percentage of hits for MEPs with amplitudes lower than 100 microvolts (78, 02 % versus 47, 83 %). Significant differences have been found in the percentage deviation index (PDI) for MEP latency estimation between the proposed algorithm and the standard method using manual assessment as reference. The main significant finding was lower PDI for MEP latency estimation of the proposed algorithm in comparison to the standard model, referring to MEP signals with amplitudes lower than 100 µV. Conclusions: The study present a new algorithm for MEP latency estimation and validation which was conducted to prove its efficacy. We believe that a new algorithm for MEP latency estimation will be an additional armamentarium in MEP latency estimation, especially when analysing signals with low MEP amplitudes values, i.e. low S/N ratio. References [1] Nguyen D. T. A., Rissanen S. M., Julkunen P., Kallioniemi E., and Karjalainen P. A. Principal Component Regression on Motor Evoked Potential in Single- pulse Transcranial Magnetic Stimulation (2019), IEEE Transactions on Neural Systems and Rehabilitation Engineering, https://doi.org/10.1109/TNSRE.2019.2923724. [2] Shivakeshavan, R. G., Disha, G., Ajay, P., Mishra, Asht M., M., Hill N., J., Carmel, J. B. G. D., Motometrics: A Toolbox for Annotation and Efficient Analysis of Motor Evoked Potentials, (2019), Frontiers in Neuroinformatics, Vol. 13, https://doi.org/10.3389/fninf.2019.00008 [3] MEPHunter, a Free Software for Signal Visualization and Analysis (2014)., Innovation and Dissemination Center for Neuromathematics (NeuroMat), https://neuromat.numec.prp.usp.br/content/mephu nter-a-free-software-for-signal-visualization- and-analysis/ [4] Jackson N., Greenhouse, I., VETA: An Open- Source Matlab-Based Toolbox for the Collection and Analysis of Electromyography Combined With Transcranial Magnetic Stimulation (2019), Frontiers in Neuroscience, Vol. 13, Art. 975, https://doi.org/10.3389/fnins.2019.00975 [5] Harquel, S., Beynel, L., Guyader, N., Marendaz, C., David, O., and Chauvin, A. (2016). CortExTool: a toolbox for processing motor cortical excitability measurements by transcranial magnetic stimulation. Available at: https://hal. archives-ouvertes.fr/hal- 01390016 [6] Mullins, C. R., Hanlon, C. MAVIN: An Open- Source Tool for Interactive Analysis and Visualization of EMG Data, (2016), Brain Stimulation, Vol. 09, Issue 02, pp. 305-306., https://doi.org/10.1016/j.brs.2015.11.009

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Interdisciplinarne tehničke znanosti, Temeljne medicinske znanosti

Napomena
Iz originalnog sažetka Figure 1 nije umentnuta kao
ni Figure 1 caption.

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