Taylor Sara, Jaques Natasha, Chen Weixuan, Fedor Szymon, Sano Akane, Picard Rosalind
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:1934-7. doi: 10.1109/EMBC.2015.7318762.
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.
最近，可穿戴设备已能够对皮肤电活动（EDA）进行长期动态测量。尽管动态记录可能存在噪声，并且在分析过程中记录伪迹很容易被误认为是生理反应，但迄今为止，尚无自动检测伪迹的方法。本文描述了一种用于自动检测EDA伪迹的机器学习算法的开发，并对分类性能进行了实证评估。我们已将结果编码到一个免费的基于网络的工具中，用于伪迹和峰值检测。
