VitalPy: A Vital Signal Analysis Package

Currently, the package supports PPG preprocessing and extraction of more than 400 features. The PPG pipeline was originally implemented for analysis of the AuroraBP database.

It provides:

• PPG preprocessing: Singal quality metrics, baseline extraction, etc.
• PPG feature extraction: time-domain, frequency-domain, statistical features ( >400 features)
• Compatibility with PPG recorded from 128 Hz to 500 Hz: tested with local devices and large datasets.

Use

VitalPy is written in Python (3.9+). Navigate to the Python repository and install the required packages:

pip install -r requirements.txt

Import:

from src.ppg.PPGSignal import PPGSignal

Check the signal (make sure that the file waveform_df is in dataframe format and contains the columns 't' for time and 'ppg' for the signal values):

signal = PPGSignal(waveform_df, verbose=1)

signal.check_keypoints()

Get features:

signal = PPGSignal(waveform_df, verbose=0)

features = signal.extract_features()

Example plots for a AuroraBP signal

Used file: measurements_oscillometric/o001/o001.initial.Sitting_arm_down.tsv

Mean signal

The following figure shows the mean template computed from all templates within the signal given as input.

Preprocessing steps

All preprocessing steps are depicted. The final result should have filtered out all low quality waveforms.

PPG Keypoints

Exemplary PPG keypoint extraction.

License

VitalPy is available under the General Public License v3.0.

Citation

If you use this repository or any of its components and/or our paper as part of your research, please cite the publication as follows:

A. Cisnal, Y. Li, B. Fuchs, M. Ejtehadi, R. Riener and D. Paez-Granados, "Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring," in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2024.3411693.

@ARTICLE{10552318,
  author={Cisnal, Ana and Li, Yanke and Fuchs, Bertram and Ejtehadi, Mehdi and Riener, Robert and Paez-Granados, Diego},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={Robust Feature Selection for BP Estimation in Multiple Populations: Towards Cuffless Ambulatory BP Monitoring}, 
  year={2024},
  volume={},
  number={},
  pages={1-12},
  keywords={Feature extraction;Estimation;Statistics;Sociology;Noise;Morphology;Monitoring;Cuffless blood pressure;photoplethysmography;pulse wave analysis},
  doi={10.1109/JBHI.2024.3411693}}