An extensible, open-source, and web-based signal quality assessment pipeline for ambulatory physiological data

---

PhysioView (formerly HeartView) is a Python-based signal processing and quality assessment pipeline with an interactive dashboard designed for wearable electrocardiograph (ECG), photoplethysmograph (PPG), and electrodermal activity (EDA) data collected in research settings.

In contrast to other existing tools, PhysioView provides an open-source graphical user interface intended to increase efficiency and accessibility for a wider range of researchers who may not otherwise be able to perform rigorous signal processing and quality checks programmatically.

PhysioView serves as both a diagnostic tool for evaluating data before and after artifact correction, and as a platform for post-processing physiological signals. We aim to help researchers make more informed decisions about data cleaning and processing procedures and the reliabiltiy of their data when wearable biosensor systems are used.

Currently, PhysioView works with data collected from the Actiwave Cardio, Empatica E4, and other devices outputting data in comma-separated value (CSV) format.

Features

• File Reader
  Read and transform raw ECG, PPG, EDA, and accelerometer data from European Data Format (EDF), archive (ZIP), and CSV files.
• Configuration File Exporter
  Define and save pipeline parameters in a JSON configuration file that can be loaded for use on the same dataset later.
• Signal Filters
  Filter out noise from baseline wander, muscle (EMG) activity, and powerline interference from your physiological signals.
• Peak Detection
  Extract heartbeats from ECG/PPG and skin conductance responses from EDA data.
• Visualization Dashboard
  View and interact with our signal quality assessment charts and signal plots of physiological time series, including preprocessed signals and their derived features (e.g., IBI, phasic and tonic components).
• Signal Quality Metrics
  Generate segment-by-segment signal quality metrics.
• Automated Beat Correction
  Apply a beat correction algorithm [1] to automatically correct artifactual beats.
• Manual Beat Editor
  Manually edit beat locations in cardiac signals.

Citation

If you use this software in your research, please cite this paper. 💛

@inproceedings{Yamane2024,
  author    = {Yamane, N. and Mishra, V. and Goodwin, M.S.},
  title     = {HeartView: An Extensible, Open-Source, Web-Based Signal Quality Assessment Pipeline for Ambulatory Cardiovascular Data},
  booktitle = {Pervasive Computing Technologies for Healthcare. PH 2023},
  series    = {Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering},
  volume    = {572},
  year      = {2024},
  editor    = {Salvi, D. and Van Gorp, P. and Shah, S.A.},
  publisher = {Springer, Cham},
  doi       = {10.1007/978-3-031-59717-6_8},
}

Latest Release [PhysioView 1.0]

This is the first official release of PhysioView 🎉

PhysioView is the renamed and updated continuation of HeartView. Recent enhancements made under HeartView are highlighted below, along with new features introduced in PhysioView.

Pipeline Enhancements:

• Introduced batch processing support.
• Added postprocessing features, including heart rate variability (HRV) extraction.
• Added automated beat correction functionality.
• Introduced EDA processing and signal quality assessment support.

Dashboard Improvements:

• Enabled uploading of ZIP archives for batch processing.
• Added a dropdown menu to select a subject's data from a batch.
• Enabled Beat Editor access within a modal.
• Added rendering of automated and manual beat corrections directly in the dashboard's signal plot and SQA charts.
• Introduced export functionality for postprocessed data.

Beat Editor Improvements:

• Simplified the interface by removing static text and file names.

This release consolidates the improvements from HeartView's final updates and marks the beginning of PhysioView as its own versioned project.

For a full list of changes, see the full changelog.

Installation

1. Clone the PhysioView GitHub repository into a directory of your choice.

cd <directory>  # replace <directory> with your directory

git clone https://github.com/cbslneu/physioview.git

2. Set up and activate a virtual environment using Python 3.9 through 3.12 inside the physioview project directory.
   ❗️Note: If you do not have virtualenv installed, run pip3 install virtualenv before proceeding below.

cd physioview

virtualenv venv -p python3

If you are a Mac/Linux user:

source venv/bin/activate

If you are a Windows user:

venv\Scripts\activate

3. Install all project dependencies:

pip3 install -r requirements.txt

Installation for Beat Editor

The Beat Editor requires Node (v20.x.x +). Please be sure to install Node before proceeding with the installation below.

Run the following code below to check if Node is installed on your machine:

node --version

If an error occurs, please refer to this link to install Node on your machine: https://nodejs.org/en/download/package-manager

1. Go to the beat-editor directory:

cd beat-editor

2. Install the required modules for the Beat Editor:

npm install

3. Go to the server folder:

cd server

4. Install the required modules for the Beat Editor's backend:

npm install

PhysioView Dashboard

Executing

1. Within the activated virtual environment (i.e., source <directory>/physioview/venv/bin/activate), run the command:

python3 app.py

2. Open your web browser and go to: http://127.0.0.1:8050/

PhysioView Beat Editor

Executing

1. Navigate to the beat-editor/server directory and start the backend:

cd beat-editor/server

npm start

2. Open another terminal tab or window and navigate back to beat-editor/. Once there, run npm start again to start the front end.

Terminating

1. Kill the dashboard program: press CTRL+c.
2. Exit the virtual environment: deactivate.