A Novel Latency Estimation Algorithm of Motor Evoked Potential Signals

Evaluation of motor evoked potential (MEP) signals elicited by transcranial magnetic stimulation (TMS) over the motor cortex provide a measure of cortico-motor excitability at the time of stimulation. In the research and clinical medical practice, the MEP latency is a relevant neurophysiological parameter to determine conduction time for neural impulses from the cortex to peripheral muscles. State changes at different levels of signal propagation through the neural tissue can significantly influence MEP latency, based on which different medical diagnoses can be issued. This study aims to present the Squared Hard Threshold Estimator (SHTE), which is a novel and improved algorithm for MEP latency estimation. Analyses presented in the paper were based on the SHTE algorithm, which was efficiently applied to a large number of MEP signals recorded from hand muscles. The SHTE algorithm was compared with other prominent methods such as the absolute hard threshold estimation (AHTE) algorithm, the statistical measures (SM) algorithm, and manual assessment. Results obtained in terms of robustness test and statistical analysis show that the proposed SHTE algorithm is reliable in estimating MEP latency, especially for the MEP signals having peak-to-peak (PTP) amplitudes lower than one hundred microvolts. Compared with the AHTE and SM algorithms, the SHTE shows a lower percentage deviation index in MEP latency estimation of the MEP signals with the PTP amplitudes lower than one hundred microvolts. Hence, the proposed SHTE algorithm represents an improved armamentarium in automatic MEP latency estimation.