An Unsupervised Machine Learning Paradigm for Artifact Removal from Electrodermal Activity in an Uncontrolled Clinical Setting

Abstract

ObjectiveElectrodermal activity (EDA) reflects sympathetic nervous system activity through sweating-related changes in skin conductance. To enable EDA data to be used robustly in clinical settings, we need to develop artifact detection and removal frameworks that can handle the types of interference experienced in clinical settings. MethodsWe collected EDA data from 69 subjects while they were undergoing surgery in the operating room. We then built an artifact removal framework using unsupervised learning methods and informed features to remove the heavy artifact that resulted from the use of surgical electrocautery during the surgery and compared it to other existing methods for artifact removal from EDA data. ResultsOur framework was able to remove the vast majority of artifact from the EDA data across all subjects with high sensitivity (94%) and specificity (90%). In contrast, existing methods used for comparison struggled to be sufficiently sensitive and specific, and none effectively removed artifact even if it was identifiable. In addition, the use of unsupervised learning methods in our framework removes the need for manually labeled datasets for training. ConclusionOur framework allows for robust removal of heavy artifact from EDA data in clinical settings such as surgery. Since this framework only relies on a small set of informed features, it can be expanded to other modalities such as ECG and EEG. SignificanceRobust artifact removal from EDA data is the first step to enable clinical integration of EDA as part of standard monitoring in settings such as the operating room.