Physiological Signal Classification

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 to project repository:

Goals for Brainhack Donostia 2021: Easy:

First tasks: The project has 3 phases:

  1. Exploration: download the data and visualize several signals to get an idea of how they look like.
  2. Feature engineering: try to find features that discern the signals (using any programming language). You can create features:
  1. Implementation: build the final classification pipeline in Python with the best features.

Communication channels:

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


What will participants learn: Basic time-series processing (normalization, filtering, maxima location, …). Time-series classification (feature engineering, validation, …).

Jan 1, 0001 12:00 AM