A High-Quality and Robust Intravascular Electromyography (iEMG) Acquisition Method for Locomotor Tasks

• Published in: IEEE Transactions on Neural Systems and Rehabilitation Engineering
• Main Authors: Ying Du, Gan Liu, Yudong Ma, Sining Li, Yahan Duan, Wenzhi Wang, Feng Duan
• Format: Article
• Language: English
• Published: IEEE 2026-01-01
• Subjects: Electromyography (EMG); intravascular electromyography (iEMG); surface electromyography (sEMG); electromyography acquisition; signal analysis
• Online Access: https://ieeexplore.ieee.org/document/11454644/
• ISSN: 1534-4320; 1558-0210

Electromyography (EMG) is essential in medical and rehabilitation fields for assessing neuromuscular functions. However, mainstream traditional surface EMG (sEMG) is susceptible to electrode displacement or noise interference during walking, leading to lower signal quality and limited long-term stability, which constrains its broader application. To overcome these limitations, we propose a novel intravascular electromyography (iEMG) acquisition method. By a minimally invasive surgery, a self-expanding stent sensor with microelectrode and lead was implanted into the femoral vein adjacent to the tibialis anterior muscle to record deep muscle activity. In this study, sEMG and iEMG were simultaneously acquired from sheep hindlimb muscle in standing state and walking state for comparative analysis of their electrophysiological properties. Level walking served as the dynamic locomotor task in this work. Totally, this acquisition experiment lasted for three days. In standing state, both EMG exhibit a high correlation (Spearman&#x2019;s <inline-formula> <tex-math notation="LaTeX">$\rho ={0}.{9018}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${p}\lt {0}.{001}$ </tex-math></inline-formula>). In walking state, iEMG demonstrates a 10.04% higher signal-to-noise ratio compared to sEMG. Additionally, iEMG shows a 33.82% lower coefficient of variation in power spectral density than sEMG, indicating a 1.51-fold improvement in signal stability. These results demonstrate that our iEMG acquisition method enables high-quality and robust recordings for long-term monitoring during walking, which provides a reliable foundation for clinical rehabilitation applications requiring precise, long-term muscle activity tracking.