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.
2019
Bizzego, A.; Battisti, A.; Gabrieli, G.; Esposito, G.; Furlanello, C.
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).
File in questo prodotto:
File Dimensione Formato  
bizzego2019pyphysio.pdf

Solo gestori archivio

Descrizione: main article
Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 833.41 kB
Formato Adobe PDF
833.41 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/246247
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 40
  • ???jsp.display-item.citation.isi??? 36
social impact