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|Title:||pyphysio: A physiological signal processing library for data science approaches in physiology||Authors:||Bizzego, Andrea
Physiological Signal Processing
|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
|ISSN:||2352-7110||DOI:||https://doi.org/10.1016/j.softx.2019.100287||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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