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.
Pyphysio: a physiological signal processing library for data science approaches in physiology / Bizzego, A.; Battisti, A.; Gabrieli, G.; Esposito, G.; Furlanello, C.. - In: SOFTWAREX. - ISSN 2352-7110. - 2019:(2019).
Pyphysio: a physiological signal processing library for data science approaches in physiology.
Bizzego A.;Esposito G.;Furlanello C.
2019-01-01
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.File | Dimensione | Formato | |
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