Sparse representation of electrodermal activity with knowledge-driven dictionaries.

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 t

SUBMITTER: Chaspari T 

PROVIDER: S-EPMC4362752 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature