TY - CHAP
T1 - PySiology
T2 - A Python Package for Physiological Feature Extraction
AU - Gabrieli, Giulio
AU - Azhari, Atiqah
AU - Esposito, Gianluca
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2020.
PY - 2020
Y1 - 2020
N2 - Physiological signals have been widely used to measure continuous data from the autonomic nervous system in the fields of computer science, psychology, and human–computer interaction. Signal processing and feature estimation of physiological measurements can be performed with several commercial tools. Unfortunately, those tools possess a steep learning curve and do not usually allow for complete customization of estimation parameters. For these reasons, we designed PySiology, an open-source package for the estimation of features from physiological signals, suitable for both novice and expert users. This package provides clear documentation of utilized methodology, guided functionalities for semi-automatic feature estimation, and options for extensive customization. In this article, a brief introduction to the features of the package, and to its design workflow, are presented. To demonstrate the usage of the package in a real-world context, an advanced example of image valence estimation from physiological measurements (ECG, EMG, and EDA) is described. Preliminary tests have shown high reliability of feature estimated using PySiology.
AB - Physiological signals have been widely used to measure continuous data from the autonomic nervous system in the fields of computer science, psychology, and human–computer interaction. Signal processing and feature estimation of physiological measurements can be performed with several commercial tools. Unfortunately, those tools possess a steep learning curve and do not usually allow for complete customization of estimation parameters. For these reasons, we designed PySiology, an open-source package for the estimation of features from physiological signals, suitable for both novice and expert users. This package provides clear documentation of utilized methodology, guided functionalities for semi-automatic feature estimation, and options for extensive customization. In this article, a brief introduction to the features of the package, and to its design workflow, are presented. To demonstrate the usage of the package in a real-world context, an advanced example of image valence estimation from physiological measurements (ECG, EMG, and EDA) is described. Preliminary tests have shown high reliability of feature estimated using PySiology.
KW - Heart rate variability
KW - Physiology
KW - Signal processing
UR - https://www.scopus.com/pages/publications/85073158408
U2 - 10.1007/978-981-13-8950-4_35
DO - 10.1007/978-981-13-8950-4_35
M3 - Chapter
AN - SCOPUS:85073158408
T3 - Smart Innovation, Systems and Technologies
SP - 395
EP - 402
BT - Smart Innovation, Systems and Technologies
PB - Springer Science and Business Media Deutschland GmbH
ER -