Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81261
Title: pyphysio: A physiological signal processing library for data science approaches in physiology
Authors: Bizzego, Andrea
Battisti, Alessandro
Gabrieli, Giulio
Esposito, Gianluca
Furlanello, Cesare
Keywords: Social sciences::Psychology
Physiological Signal Processing
Psychophysiology
Issue Date: 2019
Source: Bizzego, A., Battisti, A., Gabrieli, G., Esposito, G., & Furlanello, C. (2019). pyphysio: A physiological signal processing library for data science approaches in physiology. SoftwareX, 10100287-. doi:10.1016/j.softx.2019.100287
Series/Report no.: SoftwareX
Abstract: The lack of open-source tools for physiological signal processing hinders the development of standardized pipelines in physiology. Researchers usually must rely on commercial software that, by implementing black-box algorithms, undermines the control on the analysis and prevents the comparison of the results, ultimately affecting the scientific reproducibility. We introduce pyphysio as a step towards a data science approach oriented to compute physiological indicators, in particular of the Autonomic Nervous System activity. pyphysio serves as a basis for machine learning modules and it implements a suite of combinable algorithms for processing of signals from either by wearable or medical-grade quality devices.
URI: https://hdl.handle.net/10356/81261
http://hdl.handle.net/10220/50394
ISSN: 2352-7110
DOI: 10.1016/j.softx.2019.100287
Schools: School of Social Sciences 
Rights: © 2019 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SSS Journal Articles

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