wearablehrv is a Python package that comes in handy if you want to validate wearables and establish their accuracy in terms of heart rate (HR) and heart rate variability (HRV). wearablehrv is a complete and comprehensive pipeline that helps you go from your recorded raw data through all the necessary pre-processing steps, data analysis, and many visualization tools with graphical user interfaces.

Documentation

For the complete documentation of the API and modules, visit:

Examples

Getting Started

• Installation guide for wearablehrv
• An overview of main functionalities

Individual Pipeline

• How to prepare your data for the individual pipeline
• Preprocess your data
• Analyze your data
• Plot your data
• Learn more about the compatibility of wearablehrv with other platforms (Labfront, VU-AMS, Empatica)

Group Pipeline

• How to prepare your data for the group pipeline
• Determine the signal quality of your wearables
• Perform four major statistical analyses to determine validity
• Descriptive plots for your group data

You can also explore the example notebooks directly in your browser without installing any packages by using Binder. Simply click the badge below to get started:

User Installation

The package can be easily installed using pip:

pip install wearablehrv

The repository can be cloned:

git clone https://github.com/Aminsinichi/wearable-hrv.git

Development

wearablehrv was developed by Amin Sinichi, during his PhD at Vrije Universiteit Amsterdam.

Contributors

• Dr. Martin Gevonden
• Prof dr. Lydia Krabbendam

How to Cite:

The package is published in the Journal of Open Source Software (JOSS). Please cite it as follows:

Sinichi et al., (2024). WearableHRV: A Python package for the validation of heart rate and heart rate variability in wearables. Journal of Open Source Software, 9(100), 6240, https://doi.org/10.21105/joss.06240

Overview

The package is divided into two broad ranges of functionalities:

• Individual Pipeline: You use it for a single participant to process your raw data.
• Group Pipeline: You use it when you have multiple participants, and you have processed them through the Individual Pipeline.

Below, we offer a quick overview of the main functionalities.

Data Collection

When one wants to establish the validity of a wearable, let's say a smartwatch, that records heart rate and heart rate variability, they should use a "ground truth" device. This is usually a gold-standard electrocardiography (ECG) that measures HR and HRV accurately.

Note: We call this gold-standard a "criterion" device in our pipeline.

Then, a participant wears this ECG, together with the smartwatch, and starts recording data simultaneously. It is beneficial if we test the subject in various conditions, so we get a better sense of how well the device works.

Usually, validating multiple devices at once is a cumbersome task, requiring a lot of data preparation, processing, different alignments, etc. A powerful feature in wearablehrv is that it does not matter how many devices in how many conditions you want to test a participant! You just record your data, and the pipeline walks you through this data to the final decision on whether a device is accurate compared to the ground truth or not.

This is how your experiment may look like: a participant wearing a few wearables named Kyto, Heartmath, Empatica, Rhythm, together with a gold-standard ECG (VU-AMS), with electrodes on the chest, and will perform different tasks in different conditions (e.g., sitting for 5 minutes, standing up for 3 minutes, walking for 3 minutes, and biking for 3 minutes, while having all the devices on):

1. Individual Pipeline

1.1 Prepare Data

It is easy to read your data and experimental events with the pipeline from all your devices in one go.

```python

Importing Module

import wearablehrv

downloading some example data

path = wearablehrv.data.downloaddataandgetpath()

Define the participant ID

pp = "test"

Define your experimental conditions, for instance, sitting, standing, walking, and biking

conditions = ['sitting', 'standing', 'walking', 'biking']

Define the devices you want to validate against the criterion.

devices = ["kyto", "heartmath", "rhythm", "empatica", "vu"]

Redefine the name of the criterion device

criterion = "vu"

Read data, experimental events, and segment the continuous data into smaller chunks

data = wearablehrv.individual.importdata (path, pp, devices) events = wearablehrv.individual.defineevents (path, pp, conditions, alreadysaved= True, saveascsv= False) datachopped = wearablehrv.individual.chop_data (data, conditions, events, devices) ```

1.2 Preprocess Data

You have various methods to properly preprocess your raw data.

Correct the Lag, Trim Data

With a user-friendly GUI, correct the lag between devices, align data by cropping the beginning and the end of each of your devices, and have full control over each device and condition.

python wearablehrv.individual.visual_inspection (data_chopped, devices, conditions,criterion)

Detect Outliers and Ectopic Beats

Easily perform different types of detection methods for each device and in each condition. This is an important advantage that allows you to easily run this within a condition, for a specific device, to make the preprocessing independent.

python data_pp, data_chopped = wearablehrv.individual.pre_processing (data_chopped, devices, conditions, method="karlsson", custom_removing_rule = 0.25, low_rri=300, high_rri=2000)

Diagnostic Plots

Check how well you performed the preprocessing by comparing the detected outliers in the criterion and your selected device.

python wearablehrv.individual.ibi_comparison_plot(data_chopped, data_pp, devices, conditions, criterion, width=20, height=10)

1.3 Analyze and Plot

Easily calculate all relevant outcome variables (e.g., RMSSD, mean HR, frequency domain measures) in all your devices and conditions, and use various plotting options.

python time_domain_features, frequency_domain_features = wearablehrv.individual.data_analysis(data_pp, devices, conditions) wearablehrv.individual.bar_plot(time_domain_features, frequency_domain_features, devices, conditions, width=20, height=25, bar_width = 0.15)

2. Group Pipeline

2.1 Prepare Data

Easily load all processed data that you have put through the Individual Pipeline.

python wearablehrv.data.clear_wearablehrv_cache() path = wearablehrv.data.download_data_and_get_path(["P01.csv", "P02.csv", "P03.csv", "P04.csv", "P05.csv", "P06.csv", "P07.csv", "P08.csv", "P09.csv", "P10.csv"]) conditions = ['sitting', 'standing', 'walking', 'biking'] devices = ["kyto", "heartmath", "rhythm", "empatica", "vu"] criterion = "vu" features = ["rmssd", 'mean_hr', 'nibi_after_cropping', 'artefact'] data, file_names = wearablehrv.group.import_data(path, conditions, devices, features) # Select the features you are interested in data = wearablehrv.group.nan_handling(data, devices, features, conditions)

2.2 Signal Quality

A powerful tool to assess and report signal quality in all your wearables, in all conditions. You just need to define a few thresholds.

python data, features, summary_df, quality_df = wearablehrv.group.signal_quality(data, path, conditions, devices, features, criterion, file_names, ibi_threshold = 0.30, artefact_threshold = 0.30) wearablehrv.group.signal_quality_plot2(summary_df, condition_selection=False, condition=None)

2.3 Statistical Analysis

Perform four of the most common statistical methods for validation, and create plots, again, for all your devices, in all conditions, just by running a few functions.

Mean Absolute Percentage Error

python mape_data = wearablehrv.group.mape_analysis(data, criterion, devices, conditions, features) wearablehrv.group.mape_plot(mape_data, features, conditions, devices)

Regression Analysis

python regression_data = wearablehrv.group.regression_analysis(data, criterion, conditions, devices, features, path) wearablehrv.group.regression_plot(regression_data, data, criterion, conditions, devices, features, marker_color='red', width=10, height_per_condition=4)

Intraclass Correlation Coefficient

python icc_data = wearablehrv.group.icc_analysis(data, criterion, devices, conditions, features, path, save_as_csv=False) wearablehrv.group.icc_plot(icc_data, conditions, devices, features)

Bland-Altman Analysis

python blandaltman_data = wearablehrv.group.blandaltman_analysis(data, criterion, devices, conditions, features, path, save_as_csv=False) wearablehrv.group.blandaltman_plot(data, criterion, conditions, devices, features)

2.4 Descriptive Plots

There are many options for you to meaningfully plot your group data and make an informed decision on the accuracy of your devices.

python wearablehrv.group.violin_plot (data, conditions, features, devices)

Questions

For any questions regarding the package, please contact:

• aminsinichi@gmail.com
• m.sinichi@vu.nl