Work

Automatic identification of artifacts in electrodermal activity data

Year: 2015

Type: conference-paper

Abstract: Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.

Source: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

DOI: 10.1109/embc.2015.7318762

Authors: Sara Taylor, Natasha Jaques, Weixuan Chen, Szymon Fedor, Akane Sano, Rosalind Picard

Institution: Massachusetts Institute of Technology

Language: English

Topic: Emotion and Mood Recognition

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institute of General Medical Sciences