Abstract
F-waves are used in motor unit number estimation (MUNE) studies, which require rapid dedicated software to perform calculations. The aim of this study is to define a mathematical method for a fully automated F-wave extraction algorithm to perform F-wave and MUNE studies while performing baseline corrections without distorting traces. Ten recordings from each class, such as healthy controls, polio patients and ALS patients, were included. Submaximal stimuli were applied to the median and ulnar nerves to record 300 traces from the abductor pollicis brevis and abductor digiti minimi muscles. The autocorrelation function and the signal of sum of all traces were used to find the location for the maximum amplitude of the F-waves. F-waves were revealed by using a cutting window. Linear line estimation was preferred for baseline corrections because it did not cause any distortion in the traces. The algorithm automatically revealed F-waves from all 30 recordings in accordance with the locations marked by a neurophysiologist. The execution of the algorithm was less than 2 (usually < 1) minutes when 300 traces were analyzed. Mean sMUP amplitudes and MUNE values are important for differentiating healthy controls from patients. Moreover, F-wave parameters belonging to polio patients on whom there was a relatively low number of studies conducted were also evaluated.