Project description:
Physiopy is is a python3 suite to format and analyse physiological recordings. One of the current development goals is to implementing an automatic signal classificator that, given a signal as input, is be able to determine the type of the signal.In this project we provide time-series data of 4 kinds of physiological signals (cardiac, respiratory chest, O2 and CO2) and the goal will be to collaborate to find robust features that allow discerning between them.
Data to use:
Data to be used:
4 type of signals (cardiac, respiratory chest, O2 and CO2)- 240 time-series (60x4) recordings of 500 seconds long
2 files: time_series.csv (time-series data) and annotations.csv (time-series labels)
Credit to collaborators:
Physiopy adopts the all-contributors system to recognise contributions. Contributors will be recognised as such in the relevant library README and as authors during outreach (conference posters, talks, ...).
Type of project:
Coding methods, Method development
Development status:
Concept but no content
Programming languages:
Julia, Matlab, Python, R
Necessary Programming skills level for the project:
Familiar
Necessary git skills level for the project:
None
Modality:
ECG, physiology
Software suites:
physiopy
Email:
davidrb093g@gmail.com
What will participants learn:
Basic time-series processing (normalization, filtering, maxima location, ...).
Time-series classification (feature engineering, validation, ...).
Project title: Physiological Signal Classification
Leader: David Romero-Bascones (@drombas)
Collaborators: Stefano Moia
Topic: Machine learning, Physiology, Time-series analysis
Project description: Physiopy is is a python3 suite to format and analyse physiological recordings. One of the current development goals is to implementing an automatic signal classificator that, given a signal as input, is be able to determine the type of the signal.In this project we provide time-series data of 4 kinds of physiological signals (cardiac, respiratory chest, O2 and CO2) and the goal will be to collaborate to find robust features that allow discerning between them.
Data to use: Data to be used:
Link: https://www.dropbox.com/sh/3y5lhpn09qiz4my/AABKmpFuaGP_aHqxsAJ6LVwza?dl=0
Link to project repository: https://github.com/drombas/BHD-physiological-classification
Goals for Brainhack Donostia 2021: Easy:
First tasks: The project has 3 phases:
Communication channels: https://mattermost.brainhack.org/brainhack/channels/physiopy
Video channel: Zoom
Number of collaborators: More
Credit to collaborators: Physiopy adopts the all-contributors system to recognise contributions. Contributors will be recognised as such in the relevant library README and as authors during outreach (conference posters, talks, ...).
Type of project: Coding methods, Method development
Development status: Concept but no content
Programming languages: Julia, Matlab, Python, R
Necessary Programming skills level for the project: Familiar
Necessary git skills level for the project: None
Modality: ECG, physiology
Software suites: physiopy
Email: davidrb093g@gmail.com
What will participants learn: Basic time-series processing (normalization, filtering, maxima location, ...). Time-series classification (feature engineering, validation, ...).