The latest version of the library can be found at https://gitlab.com/a.bizzego/pyphysio

With many new features, such as: support for multi-channel/multi-component data with parallelization (e.g. EEG, fNIRS), novel signal processing algorithms, signal quality indicators, etc.

Please note that this repository is not actively maintained anymore.

pyphysio

pyphysio is a library of state of art algorithms for the analysis of physiological signals. It contains the implementations of the most important algorithms for the analysis of physiological data like ECG, BVP, EDA, inertial, and fNIRS.

Signals

To allow optimization, two wrapper classes for signals are provided:

• EvenlySignal: wraps a signal sampled with a constant frequency, specifying its values (samples) and its _samplingfreq
• UnevenlySignal: wraps a signal where the distance between samples is not constant, specifying the values, the original _samplingfreq and the _xvalues that can be (_xtype) 'instants' or 'indices' wrt the original signal.

Classes of Algorithms

Every algorithm is available under the main module name e.g.

import pyphysio as ph
ph.IIRFilter(...)

however they are divided into the following groups:

• estimators: from a signal produce a signal of a different type
• filters: from a signal produce a filtered signal of the same type
• indicators: from a signal produce a value
• segmentation: from a signal produce a series of segments
• tools: from a signal produce arbitrary data

Examples

Examples on how to use pyphysio can be found at:

https://github.com/MPBA/pyphysio/tree/master/tutorials

Reference

If you use pyphysio for research, please cite us:

"Bizzego et al. (2019) 'pyphysio: A physiological signal processing library for data science approaches in physiology', SoftwareX" https://www.sciencedirect.com/science/article/pii/S2352711019301839