Abstract

Wearable technology permeates every aspect of our daily life increasing the need of reliable and interpretable models for processing the large amount of biomedical data. We propose the EDA-Gram, a multidimensional fingerprint of the electrodermal activity (EDA) signal, inspired by the widely-used notion of spectrogram. The EDA-Gram is based on the sparse decomposition of EDA from a knowledge-driven set of dictionary atoms. The time axis reflects the analysis frames, the spectral dimension depicts the width of selected dictionary atoms, while intensity values are computed from the atom coefficients. In this way, EDA-Gram incorporates the amplitude and shape of Skin Conductance Responses (SCR), which comprise an essential part of the signal. EDA-Gram is further used as a foundation for signal-specific feature design. Our results indicate that the proposed representation can accentuate fine-grain signal …

Date

2016

Authors

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

Conference

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Pages

403-406

Publisher

IEEE