Which noise reduction technique is most effective at reducing EKG artifact?

Artifact rejection is the most effective technique for reducing EKG artifact because it specifically targets and removes unwanted signals that interfere with the desired signal quality. In the context of neurophysiological monitoring, especially during surgeries, EKG artifacts can significantly mask the neural signals being recorded. Artifact rejection algorithms analyze the incoming signals and identify patterns consistent with EKG activity, allowing for the suppression of these specific artifacts while preserving the relevant neurophysiological data.

Low-pass filtering, high-pass filtering, and signal averaging can also play roles in noise reduction but are not as precisely targeted at EKG artifacts. Low-pass filtering can reduce high-frequency noise but might not effectively address the low-frequency EKG signals. High-pass filtering might remove some of the lower frequencies but could also risk losing important components of the neural signals if they fall within that range. Signal averaging can be useful for enhancing the signal-to-noise ratio, but it does not specifically target EKG interference. Thus, the tailored approach of artifact rejection makes it the best choice for minimizing EKG artifacts during intraoperative monitoring.