PySiology

Introduction

PySiology is a Python package used to analyze Physiological signals. With pysiology you can easily analyze:

Electromyographic signals
Electrocardiographic signals
Electrodermal activity signals

Installation

PySiology can be installed using pip:

pip install pysiology

or downloading / cloning the repository and, from the root folder of the project, running:

python setup.py install

Updating the package

To update the package via pip, you can use:

pip install --user --upgrade pysiology

Documentation

You can check the full documentation here: https://pysiology.rtfd.io

WARNING

Sample data are not downloaded when using Pip. Please download the samples manually from the repository (https://github.com/Gabrock94/Pysiology/tree/master/share/data) and load them using

import pickle

with open("path/to/sample/data.pkl",'rb') as f:
          data = pickle.load(f)

Example

import matplotlib.pyplot as plt #used for visualization purposes in this tutorial.

import pysiology
print(pysiology.__version__)


ECG = pysiology.sampledata.loadsampleECG() #load the sample ECG Signal
EMG = pysiology.sampledata.loadsampleEMG() #load the sample EMG Signal
GSR = pysiology.sampledata.loadsampleEDA() #load the sample GSR Signal

sr = 1000 #samplerate in Hz

#We can define the event in the way we prefer. 
#In this example I will use a 2 x nEvent matrix, containing the name of the event and the onset time.
events = [["A",10],
          ["B",20]]
eventLenght = 8 #lenght in seconds we want to use to compute feature estimation
results = {} #we will store the results in a dict for simplicity.
for event in events:
    startSample = sr * event[1] #samplerate of the signal multiplied by the onset of the event in s
    endSample = startSample + (sr * eventLenght) #Final sample to use for estimation
    results[event[0]] = {} #initialize the results
    results[event[0]]["ECG"] = pysiology.electrocardiography.analyzeECG(ECG[startSample:endSample],sr) #analyze the ECG signal
    results[event[0]]["EMG"] = pysiology.electromyography.analyzeEMG(EMG[startSample:endSample],sr) #analyze the EMG signal
    results[event[0]]["GSR"] = pysiology.electrodermalactivity.analyzeGSR(GSR[startSample:endSample],sr) #analyze the GSR signal

Cite

If you use PySiology, please cite:

Gabrieli G., Azhari A., Esposito G. (2020) PySiology: A Python Package for Physiological Feature Extraction. In: Esposito A., Faundez-Zanuy M., Morabito F., Pasero E. (eds) Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, vol 151. Springer, Singapore

Requirements

Numpy
Scipy
Peakutils
Matplotlib

Contacts

Feel free to contact me for questions, suggestions or to give me advice as well at: giulio.gabrieli@iit.it

Scientific Publications that used pysiology

Jain, A., & Kumar, R. (2024, May). Machine Learning based Anxiety Detection using Physiological Signals and Context Features. In 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT) (pp. 116-121). IEEE.
Krivich, E., Mottus, M., & Bauters, M. (2023). Comparing Psychophysiological Responses to Vicarious Pain Experience Elicited by Videos Featuring either Humans or Avatars. In Proceedings of the 26th International Academic Mindtrek Conference.
Wiercinski, T., & Zawadzka, T. (2023). Short Paper: Late Fusion Approach for Multimodal Emotion Recognition Based on Convolutional and Graph Neural Networks.
Gabrieli, G., Bornstein, M. H., Setoh, P., & Esposito, G. (2023). Machine learning estimation of users’ implicit and explicit aesthetic judgments of web-pages. Behaviour & Information Technology, 42(4), 392-402.- Hsu, S. M., Chen, S. H., &
Momota, M. M. R., Morshed, B. I., Ferdous, T., & Fujiwara, T. (2023). Fabrication and Characterization of Inkjet Printed Flexible Dry ECG Electrodes. IEEE Sensors Journal, 23(7), 7917-7928.
Warner, J., Gault, R., & McAllister, J. (2022, July). Optimised EMG pipeline for gesture classification. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 3628-3631). IEEE.
Momota, M. M. R., & Morshed, B. I. (2022). ML algorithms to estimate data reliability metric of ECG from inter-patient data for trustable AI-based cardiac monitors. Smart Health, 26, 100350.
Chan, S. H. M., Qiu, L., Esposito, G., Mai, K. P., Tam, K. P., & Cui, J. (2021). Nature in virtual reality improves mood and reduces stress: evidence from young adults and senior citizens. Virtual reality, 1-16.
Chan, S. H. M., Qiu, L., Esposito, G., & Mai, K. P. (2021). Vertical greenery buffers against stress: Evidence from psychophysiological responses in virtual reality. Landscape and Urban Planning, 213, 104127.
Huang, T. R. (2021). Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human–Robot Conversations. Sensors, 21(17), 5844.
Aqajari, S. A. H., Naeini, E. K., Mehrabadi, M. A., Labbaf, S., Rahmani, A. M., & Dutt, N. (2020). Gsr analysis for stress: Development and validation of an open source tool for noisy naturalistic gsr data. arXiv preprint arXiv:2005.01834.
Bizzego, A., Azhari, A., Campostrini, N., Truzzi, A., Ng, L. Y., Gabrieli, G., ... & Esposito, G. (2019). Strangers, friends, and lovers show different physiological synchrony in different emotional states. Behavioral Sciences, 10(1), 11.

Gabrock94/Pysiology