Empirical mode decomposition processing to improve multifocal-visual-evoked-potential signal analysis in multiple...

Area of Science:

• Neuroscience
• Ophthalmology
• Biomedical Engineering

Background:

• Multiple Sclerosis (MS) diagnosis and monitoring rely on sensitive electrophysiological measures.
• Multifocal-visual-evoked-potential (mfVEP) signals are crucial for assessing visual pathway function in MS.
• Conventional mfVEP processing may have limitations in discriminating between patient groups and reducing signal variability.

Purpose of the Study:

• To evaluate the efficacy of empirical mode decomposition (EMD) filtering for mfVEP signals.
• To enhance the discrimination of amplitude-based mfVEP signals between control and MS patient groups.
• To reduce interocular latency variability in mfVEP recordings from control subjects.

Main Methods:

• mfVEP signals were recorded from healthy controls, MS patients, and MS-risk progression groups (RIS, CIS).
• Signals were processed using conventional bandpass filtering (XDFT) and EMD-based filtering (XEMD).
• Amplitude and latency analyses were performed on full visual field and specific eccentric regions (ring 5).

Main Results:

• EMD-filtered mfVEP signals (XEMD) demonstrated higher discrimination indices compared to conventional XDFT processing.
• XEMD filtering resulted in lower interocular latency variability in control subjects.
• Optimal discrimination and latency reduction were observed in the ring 5 (9.8-15° eccentricity) of the visual field.

Conclusions:

• EMD filtering of mfVEP signals offers improved identification of individuals at risk for MS.
• This advanced signal processing enhances the accuracy of latency measurements for MS diagnosis and progression studies.
• EMD-filtered mfVEP analysis holds potential for assessing visual cortex activity in MS management.