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Automatic selection and feature extraction of motor-evoked potentials by transcranial magnetic stimulation in stroke patients

Automatic selection and feature extraction of motor-evoked potentials by transcranial magnetic stimulation in stroke patients

Authors: 
Jose E. Tecuapetla-Trejo, Jessica Cantillo-Negrete, Paul Carrillo-Mora, Raquel Valdés-Cristerna, Emmanuel Ortega-Robles, Oscar Arias-Carrion, Ruben I. Carino-Escobar
Year: 
2021
Journal: 
Medical & Biological Engineering & Computing
Abstract: 

Transcranial magnetic stimulation (TMS) allows the assessment of stroke patients’ cortical excitability and corticospinal tract integrity, which provide information regarding motor function recovery. However, the extraction of features from motor-evoked potentials (MEP) elicited by TMS, such as amplitude and latency, is performed manually, increasing variability due to observer-dependent subjectivity. Therefore, an automatic methodology could improve MEP analysis, especially in stroke, which increases the difficulty of manual MEP measurements due to brain lesions. A methodology based on time-frequency features of stroke patients’ MEPs that allows to automatically select and extract MEP amplitude and latency is proposed. The method was validated using manual measurements, performed by three experts, computed from patients’ affected and unaffected hemispheres. Results showed a coincidence of 58.3 to 80% between automatic and manual MEP selection. There were no significant differences between the amplitudes and latencies computed by two of the experts with those obtained with the automatic method, for most comparisons. The median relative error of amplitudes and latencies computed by the automatic method was 5% and 23%, respectively. Therefore, the proposed method has the potential to reduce processing time and improve the computation of MEP features, by eliminating observer-dependent variability due to the subjectivity of manual measurements.

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