4 May 2016 Dictionary learning and sparse recovery for electrodermal activity analysis
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Abstract
Measures of electrodermal activity (EDA) have advanced research in a wide variety of areas including psychophysiology; however, the majority of this research is typically undertaken in laboratory settings. To extend the ecological validity of laboratory assessments, researchers are taking advantage of advances in wireless biosensors to gather EDA data in ambulatory settings, such as in school classrooms. While measuring EDA in naturalistic contexts may enhance ecological validity, it also introduces analytical challenges that current techniques cannot address. One limitation is the limited efficiency and automation of analysis techniques. Many groups either analyze their data by hand, reviewing each individual record, or use computationally inefficient software that limits timely analysis of large data sets. To address this limitation, we developed a method to accurately and automatically identify SCRs using curve fitting methods. Curve fitting has been shown to improve the accuracy of SCR amplitude and location estimations, but have not yet been used to reduce computational complexity. In this paper, sparse recovery and dictionary learning methods are combined to improve computational efficiency of analysis and decrease run time, while maintaining a high degree of accuracy in detecting SCRs. Here, a dictionary is first created using curve fitting methods for a standard SCR shape. Then, orthogonal matching pursuit (OMP) is used to detect SCRs within a dataset using the dictionary to complete sparse recovery. Evaluation of our method, including a comparison to for speed and accuracy with existing software, showed an accuracy of 80% and a reduced run time.
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Malia Kelsey, Ahmed Dallal, Safaa Eldeeb, Murat Akcakaya, Ian Kleckner, Christophe Gerard, Karen S. Quigley, Matthew S. Goodwin, "Dictionary learning and sparse recovery for electrodermal activity analysis", Proc. SPIE 9857, Compressive Sensing V: From Diverse Modalities to Big Data Analytics, 98570H (4 May 2016); doi: 10.1117/12.2227142; https://doi.org/10.1117/12.2227142
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