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dc.contributor.authorBizzego, Andreaen
dc.contributor.authorBattisti, Alessandroen
dc.contributor.authorGabrieli, Giulioen
dc.contributor.authorEsposito, Gianlucaen
dc.contributor.authorFurlanello, Cesareen
dc.identifier.citationBizzego, 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.2019.100287en
dc.description.abstractThe 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.en
dc.format.extent5 p.en
dc.rights© 2019 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (
dc.subjectSocial sciences::Psychologyen
dc.subjectPhysiological Signal Processingen
dc.titlepyphysio: A physiological signal processing library for data science approaches in physiologyen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Social Sciencesen
dc.description.versionPublished versionen
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