Fully automated F-wave corridor extraction and analysis algorithm for F-wave analyses and MUNE studies

Area of Science:

• Neurophysiology
• Biomedical Engineering
• Clinical Electrophysiology

Background:

• F-waves are essential for motor unit number estimation (MUNE) but require specialized software for analysis.
• Existing methods for F-wave extraction can be time-consuming and may lack automation.
• Accurate F-wave analysis is critical for diagnosing and monitoring neuromuscular disorders.

Purpose of the Study:

• To develop a fully automated mathematical algorithm for F-wave extraction.
• To enable rapid and accurate F-wave and MUNE studies with baseline correction.
• To evaluate the algorithm's performance in differentiating healthy controls from patients with polio and ALS.

Main Methods:

• An automated algorithm was developed using autocorrelation functions and signal summation to locate F-waves.
• Linear line estimation was employed for baseline correction to prevent trace distortion.
• The algorithm was tested on 30 recordings (10 each) from healthy controls, polio patients, and ALS patients, analyzing 300 traces per recording.

Main Results:

• The algorithm successfully and automatically identified F-waves in all 30 recordings, matching neurophysiologist markings.
• Analysis of 300 traces took less than 2 minutes, demonstrating high efficiency.
• Mean sMUP amplitudes and MUNE values derived from the algorithm effectively differentiated healthy controls from patients.

Conclusions:

• The developed automated F-wave extraction algorithm provides a rapid and accurate tool for MUNE studies.
• This method facilitates reliable differentiation between healthy individuals and patients with neuromuscular diseases like polio and ALS.
• The algorithm's efficiency and accuracy support its clinical application in neurophysiological assessments.