Key result
The pyphysio library provides an open-source, standardized data science approach for processing physiological signals and computing Autonomic Nervous System activity indicators.
Why the study?
The lack of open-source tools for physiological signal processing hinders standardized pipelines, forcing researchers to rely on commercial black-box software that limits reproducibility.
The introduction of pyphysio provides an open-source alternative to commercial black-box software for physiological signal processing, aiming to improve scientific reproducibility.
May improve reproducibility of ANS metrics in research; leaves open clinical validation before practice adoption.
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.
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Bizzego et al. (2019) studied Physiological signal processing. pyphysio vs. Commercial software was evaluated. The pyphysio library provides an open-source, standardized data science approach for processing physiological signals and computing Autonomic Nervous System activity indicators.
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