Electromyographic background noise in thyroid surgery: causes, characteristics, and mitigation strategies

Accuracy of intraoperative neuromonitoring (IONM) largely depends on the quality of electromyography (EMG) signals, which are frequently affected by background noise. EMG background noise during thyroid surgery is related to various factors including electrical interference, electrode malposition, residual neuromuscular blockade, fluid conduction, and motion artifacts. Electrical interference from electrocautery and unstable grounding, as well as poor electrode contact, were identified as the most frequent causes of background noise. Skin electrodes exhibited relatively higher baseline noise levels compared to EMG tubes or needle electrodes. Implementation of impedance monitoring, shielded cables, adaptive filters, and machine learning–based artifact classification can effectively improve the signal-to-noise ratio and diagnostic reliability of IONM. Reducing EMG background noise is critical to ensure accurate neural monitoring during thyroid surgery. Optimized threshold calibration, stable electrode fixation, and standardized intraoperative protocols are essential to minimize noise-related diagnostic errors. Future research should focus on automated noise recognition algorithms and improved electrode designs to further enhance monitoring reliability and surgical safety.