pyphysio: A physiological signal processing library for data science approaches in physiology

被引：48

作者：

Bizzego, Andrea ^{[1
]}

Battisti, Alessandro ^{[2
]}

Gabrieli, Giulio ^{[3
]}

Esposito, Gianluca ^{[1
,3
]}

Furlanello, Cesare ^{[2
]}

机构：

[1] Univ Trento, Trento, Italy

[2] Fdn Bruno Kessler, Povo, Italy

[3] Nanyang Technol Univ, Singapore, Singapore

来源：

SOFTWAREX | 2019年 / 10卷

关键词：

Physiological signal processing; Psychophysiology; Autonomic indicators; Data science; !text type='Python']Python[!/text; RATE-VARIABILITY ANALYSIS; RESPONSES;

D O I：

10.1016/j.softx.2019.100287

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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. (C) 2019 Published by Elsevier B.V.

引用

页数：5

共 26 条

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