Projects/01
1M+ downloads · 470+ citations
BioSPPy
Open-source Python toolbox for biosignal processing
I am a main developer and maintainer of BioSPPy, an open-source Python toolbox for processing, analysing, and visualising physiological signals.
PythonSignal ProcessingBiosignalsProject overview
- Problem
- Make physiological signal processing accessible through reusable software.
- My contribution
- Implemented HRV analysis and EDF reading, redesigned plots, improved documentation and review pull requests.
- Approach
- Filtering, peak detection, HRV analysis and signal-quality tools in Python.
- Constraints
- Support multiple physiological signal types within one reusable toolbox.
- Result
- 1M+ downloads and 470+ citations; published in SoftwareX (2024).
My role
I am a main developer and maintainer of BioSPPy and a co-author of its SoftwareX paper. The toolbox builds on an established open-source project and is developed with other contributors.
What it offers
BioSPPy helps researchers and engineers turn physiological recordings into processed signals, useful features, and clear visual summaries. It supports ECG, PPG, EDA, EEG, EMG, respiration, and other biosignals, with ready-to-use workflows and individual tools for custom analyses.
- Signal filtering, peak detection, segmentation, and heart-rate estimation.
- Feature extraction and heart-rate variability (HRV) analysis in the time, frequency, and non-linear domains.
- Signal quality assessment, synthetic signal generation, and plotting tools for exploring results.
ECG and HRV in practice
The ECG workflow filters a recording, detects R-peaks, extracts heartbeat templates, and estimates heart rate. Summary plots bring these outputs together for a quick visual check.
HRV analysis uses the intervals between heartbeats to describe how cardiac rhythm varies over time, combining numerical features with visual summaries.
Adoption
BioSPPy has passed 1 million downloads and has been cited in 470+ publications. The toolbox was published in SoftwareX (2024).