Abstract

Biometric sensors and portable devices are being increasingly embedded into our everyday life, creating the need for robust physiological models that efficiently represent, analyze, and interpret the acquired signals. We propose a knowledge-driven method to represent electrodermal activity (EDA), a psychophysiological signal linked to stress, affect, and cognitive processing. We build EDA-specific dictionaries that accurately model both the slow varying tonic part and the signal fluctuations, called skin conductance responses (SCR), and use greedy sparse representation techniques to decompose the signal into a small number of atoms from the dictionary. Quantitative evaluation of our method considers signal reconstruction, compression rate, and information retrieval measures, that capture the ability of the model to incorporate the main signal characteristics, such as SCR occurrences. Compared to previous …

Date

2014

Authors

Theodora Chaspari, Andreas Tsiartas, Leah I Stein, Sharon A Cermak, Shrikanth S Narayanan

Journal

IEEE Transactions on Biomedical Engineering

Volume

62

Issue

3

Pages

960-971

Publisher

IEEE