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The electrodermal activity (EDA) signal is a sensitive and non-invasivesurrogate measure of sympathetic function. Use of EDA has increased inpopularity in recent years for such applications as emotion and stressrecognition; assessment of pain, fatigue, and sleepiness; diagnosis ofdepression and epilepsy; and other uses. Recently, there have been severalstudies using ambulatory EDA recordings, which are often quite useful foranalysis of many physiological conditions. Because ambulatory monitoring useswearable devices, EDA signals are often affected by noise and motion artifacts.An automated noise and motion artifact detection algorithm is therefore ofutmost importance for accurate analysis and evaluation of EDA signals. In thispaper, we present machine learning-based algorithms for motion artifactdetection in EDA signals. With ten subjects, we collected two simultaneous EDAsignals from the right and left hands, while instructing the subjects to moveonly the right hand. Using these data, we proposed a cross-correlation-basedapproach for non-biased labeling of EDA data segments. A set of statistical,spectral and model-based features were calculated which were then subjected toa feature selection algorithm. Finally, we trained and validated severalmachine learning methods using a leave-one-subject-out approach. Theclassification accuracy of the developed model was 83.85% with a standarddeviation of 4.91%, which was better than a recent standard method that weconsidered for comparison to our algorithm.

A Preliminary Study on Automatic Motion Artifacts Detection in Electrodermal Activity Data Using Machine Learning