Characterizing repeater F waves using the compound muscle action potential scan and machine learning techniques - 09/05/25

Doi : 10.1016/j.neucli.2025.103076

Xiaoyan Li ^a,^b,^⁎ , Maoqi Chen ^c, Paul E. Barkhaus ^b, Sanjeev D. Nandedkar ^b,^d, Marek Cierny ^b, Caitlin Moore ^b, Ping Zhou ^c
^a Fischell Department of Bioengineering, University of Maryland at College Park, College Park, MD, USA
^b Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
^c School of Rehabilitation Science and Engineering, University of Health and Rehabilitation Sciences, Qingdao, PR China
^d Natus Medical, Inc, Middleton, WI, USA

^⁎Corresponding author at: Fischell Department of Bioengineering, University of Maryland at College Park, College Park, MD, USA.Fischell Department of BioengineeringUniversity of Maryland at College ParkCollege ParkMDUSA

Abstract

Objectives

The purpose of this study is to introduce a new analytical technique on repeater F waves (RFws) using compound muscle action potential (CMAP) scan and new features to differentiate the nerves innervating hand muscles.

Methods

Twenty-four healthy subjects participated in the CMAP scan study. Tests were performed on the abductor pollicis brevis (APB) and abductor digit minimi (ADM) muscles. RFws were extracted using unsupervised machine learning and analyzed through conventional characteristics such as amplitude, latency, indices of F repeaters and total F repeaters, as well by new variables, including relative stimulus intensity (ReSI), relative F/M ratio (RF/MR), and range of stimulation intensity (RaSI).

Results

Significantly higher indices of F repeaters (APB: 18.42±4.92 %, ADM: 13.58±4.68 %, p < 0.005) and total F repeaters (APB: 34.75±7.8 %, ADM: 26.67±8.56 %, p < 0.005) were observed in the APB compared with the ADM muscles. The ReSI (Z = 13.81, p < 0.001) and RaSI (Z=-2.46, p < 0.05) showed significant differences between the two muscles.

Discussion and Conclusion

This study provides normative data for RFws based on the CMAP scan recording and identifies different characteristics of repeaters between APB and ADM muscles. Findings of the study improve understanding of motoneuron physiological properties in hand muscles. The CMAP scan-based F wave analysis can be combined with motor unit number estimation technique to provide insights into surviving motoneuron function in neurological disorders.

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Key words : Compound muscle action potential (CMAP) scan, Motoneuron (MN), machine learning, hand muscles, electrophysiology, Repeater F-waves