Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81261
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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.date.accessioned2019-11-13T01:06:48Zen
dc.date.accessioned2019-12-06T14:26:48Z-
dc.date.available2019-11-13T01:06:48Zen
dc.date.available2019-12-06T14:26:48Z-
dc.date.copyright2019en
dc.date.issued2019en
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.identifier.issn2352-7110en
dc.identifier.urihttps://hdl.handle.net/10356/81261-
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.language.isoenen
dc.relation.ispartofseriesSoftwareXen
dc.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/).en
dc.subjectSocial sciences::Psychologyen
dc.subjectPhysiological Signal Processingen
dc.subjectPsychophysiologyen
dc.titlepyphysio: A physiological signal processing library for data science approaches in physiologyen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Social Sciencesen
dc.identifier.doi10.1016/j.softx.2019.100287en
dc.description.versionPublished versionen
dc.identifier.rims215093en
item.grantfulltextopen-
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