Surgical Cautery Artifact Removal from Electrodermal Activity Data

Systems and methods for identifying and removing artifacts from electrodermal activity (EDA) data are described herein. A method includes identifying artifacts in segments of EDA data using unsupervised machine learning based on feature vectors extracted from segments of the data. After the artifacts are identified, they can be removed from the EDA data. Artifact-free EDA data can be used to estimate a patient's nociceptive state, which in turn can be used to modify a dosage of anesthetic drugs administered to the patient based on this estimation.