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changed, 365 insertions(+) create mode 100644 joss.06240/10.21105.joss.06240.crossref.xml diff --git a/joss.06240/10.21105.joss.06240.crossref.xml b/joss.06240/10.21105.joss.06240.crossref.xml new file mode 100644 index 0000000000..ca82fe2ad0 --- /dev/null +++ b/joss.06240/10.21105.joss.06240.crossref.xml @@ -0,0 +1,365 @@ + + + + 20240826103509-2a473401fbdf85337c75e1933ed94871c9d146ff + 20240826103509 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 08 + 2024 + + + 9 + + 100 + + + + WearableHRV: A Python package for the validation of +heart rate and heart rate variability in wearables + + + + Mohammadamin + Sinichi + https://orcid.org/0009-0008-2491-1542 + + + Martin + Gevonden + + + Lydia + Krabbendam + https://orcid.org/0000-0003-4074-5149 + + + + 08 + 26 + 2024 + + + 6240 + + + 10.21105/joss.06240 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.12917981 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/6240 + + + + 10.21105/joss.06240 + https://joss.theoj.org/papers/10.21105/joss.06240 + + + https://joss.theoj.org/papers/10.21105/joss.06240.pdf + + + + + + A python package for heart rate variability +analysis and signal preprocessing + Robin Champseix + Journal of Open Research +Software + 9 + 10.5334/jors.305 + 2021 + Robin Champseix, C. 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NeuroKit2: A python toolbox for neurophysiological signal +processing. 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+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6240 +10.21105/joss.06240 + +WearableHRV: A Python package for the validation of heart +rate and heart rate variability in wearables + + + +https://orcid.org/0009-0008-2491-1542 + +Sinichi +Mohammadamin + + + +* + + + +Gevonden +Martin + + + + + +https://orcid.org/0000-0003-4074-5149 + +Krabbendam +Lydia + + + + + + +Department of Clinical, Neuro- & Developmental +Psychology, Faculty of Behavioural and Movement Sciences, Vrije +Universiteit Amsterdam, The Netherlands + + + + +Department of Biological Psychology, Faculty of Behavioural +and Movement Sciences, Vrije Universiteit Amsterdam, The +Netherlands + + + + +Institute Brain and Behaviour (iBBA), Amsterdam, The +Netherlands + + + + +* E-mail: + + +10 +9 +2023 + +9 +100 +6240 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +Psychophysiology +Heart rate variability +Heart rate +HRV +Wearables + + + + + + Summary +

Wearable devices that monitor physiology have become ubiquitous, + and include smart watches, smart jewellery, and smart textiles. The + market for these devices is rapidly expanding with new brands and + products. These devices measure a variety of signals, which are + translated into a large amount of different features, of which heart + rate (HR) and heart rate variability (HRV) are among the most common. + These features are particularly interesting, not only for consumers + but also for researchers, because they are predictive of mental and + physical health outcomes, and easily obtained. However, for + manufacturers, there may be a trade-off between user acceptability on + the one hand and accuracy on the other, where profit margins typically + turn out to be the decisive factor. Therefore, the following question + continually comes up: is the cardiac data recorded by this new + watch/ring/shirt accurate enough to use in research?

+

The WearableHRV Python package offers a + comprehensive pipeline for validating the accuracy of HR and HRV + measurements. It allows for advanced statistical analyses on device + agreement from beat-to-beat cardiac data. The package’s graphical user + interface (GUI) facilitates pre-processing, visualization, and data + analysis at both individual and group levels. As input, a user of + WearableHRV should use a criterion device + (i.e., a gold standard), preferably an electrocardiograph (ECG). + Simultaneously acquired data from the device(s) of interest can then + be compared against the criterion. The only required inputs for the + pipeline are the inter-beat intervals (IBIs) and timestamps for each + device; the rest is fully handled by the + WearableHRV package.

+
+ + Statement of Need +

The use of wearables in psychophysiology and sports sciences has + exponentially increased over the past decade. While the golden + standard is electrocardiography, for prolonged monitoring many studies + use devices that measure HR and HRV using Photoplethysmography (PPG). + This technique optically measures the changes in blood volume in + peripheral tissues (e.g., earlobe, wrist, arm, fingertip) and + substitutes detected pulses for the actual contraction of cardiac + ventricular muscles + (Challoner + & Ramsay, 1974). Although the PPG method is promising and + versatile, several studies have highlighted concerns about its + validity in identifying HR and HRV, especially when the user is in + motion + (Allen, + 2007; + Bent + et al., 2020; + Hill + et al., 2015; + Nederend + et al., 2017; + Pinheiro + et al., 2016; + Quintana + et al., 2016; + Schäfer + & Vagedes, 2013; + Stone + et al., 2021).

+

The oversight regarding the accuracy of these wearables may be due + to the lack of an integrated and user-friendly method for assessing + the validity of new wearables entering the market. A validation + pipeline could lead users from collecting raw data, through + pre-processing and advanced statistical analysis, to obtaining the + necessary parameters and visualizations required to assess device + agreement. The WearableHRV package was + developed to address this gap. Other existing packages such as + hrv-analysis + (Robin + Champseix, 2021), NeuroKit2 + (Makowski + et al., 2021), pyHRV + (Gomes + et al., 2019), and similar toolkits offer solutions for + pre-processing, analysis, and visualization once IBIs from a single + device are provided. However, the added value of + WearableHRV is rooted in a few key aspects:

+

First, the currently available packages are not tailored for + validation purposes. This becomes especially noticeable when + validating multiple wearables at once, across different experimental + conditions. This manual process can become a cumbersome task with the + packages currently at hand. Second, to make the validation of + wearables easier, a user-friendly solution is needed, which current + packages lack. This is particularly notable for essential validation + steps, such as correcting linear and non-linear lags between devices + or trimming signals for a specific device or condition, steps that + WearableHRV simplifies with GUIs. Third, + establishing the validity of a wearable against a gold standard + involves numerous decisions. Our pipeline provides a thorough method + with extensive documentation for users to do this. The availability of + such a pipeline also encourages researchers to contribute to + establishing a standardized validation protocol, unifying the + approach, reducing variability between methods, and facilitating + result comparisons. Finally, the division of the + WearableHRV pipeline into individual and group + pipelines offers an advantage for different types of users. The + individual pipeline is designed for processing data from a single + participant, whereas the group pipeline offers tools to establish the + quality of the signals, device agreement, and validity of the devices + across multiple participants. Most common statistical analyses in + validation studies, such as mean absolute percentage error, regression + analysis, intraclass correlation coefficient (ICC), and Bland-Altman + analysis, are already incorporated into the pipeline + (Altman + & Bland, 1983; + Bruton + et al., 2000; + Haghayegh + et al., 2020; + Makridakis, + 1993).

+

In summary, provided that a wearable device (either PPG or ECG) + allows for the export of the complete time series of recorded IBIs, + this Python package makes it relatively easy to establish the validity + of a novel wearable in just a few steps. The inclusion of GUI in most + functions grants researchers and wearable users the flexibility to + validate an unlimited number of wearables across a wide range of + conditions.

+
+ + Main Features and Basic Usage +

In this section, we provide an overview of the main functions and + basic usage of the WearableHRV pipeline. The + complete documentation of the API and modules can be found + here. + Please also refer to the + README.md, + which provides links to several Jupyter Notebook examples.

+

The pipeline is divided into two parts: the individual pipeline, + which focuses on the data from a single individual, and the group + pipeline, which combines the data from all the participants in a + single study.

+ +

A summary of the main functionalities of WearableHRV + Python package.

+ +
+ + Individual pipeline +

The individual pipeline allows for the validation of a wearable + of interest against a criterion device (e.g., a gold-standard ECG) + under different conditions. Data necessary for the pipeline should + be in a .csv file with UNIX timestamps (specified in milliseconds) + and IBIs, in two separate columns. Next, experimental conditions + need to be defined using the define_events + function. The import_data function is then + used to bring the continuous time series from all devices into the + pipeline. Data segmentation into smaller, condition-specific chunks + is achieved with the chop_data function.

+

One of the primary strengths of + WearableHRV is the + visual_inspection function with the + assistance of the GUI, which allows for simultaneous visualization + of IBIs and addresses the challenge of correcting devices’ lag when + wearables’ internal clocks are not in sync.

+ +

The black line shows the IBIs of the criterion device + (ECG). The red line represents the IBIs of a given PPG device. By + toggling the Device and + Conditions widgets, one can easily explore + other devices and conditions. The Lag + slider allows for lag correction between the devices, and it is + possible to crop a part of the signal if necessary.

+ +
+

Pre-processing and feature extraction with the + pre_processing and + data_analysis functions use the + hrv-analysis python package functionalities + (Robin + Champseix, 2021), and output numerous time domain and + frequency domain features for each condition and device. To + facilitate comparing the criterion device with the device of + interest and visualization of results, several plotting functions + are provided, including + result_comparison_plot, + bar_plot, line_plot, + radar_plot, and + unfolding_plot. All time and frequency + features for every device and condition can be exported for later + use in the group pipeline via the save_data + function.

+
+ + Group pipeline +

Moving to group-level analysis, the + import_data function of the group pipeline + aggregates .csv output files that have gone through the individual + pipeline.

+

An important step in wearable validation is quantifying signal + quality, which the signal_quality function + allows for labeling as poor, missing, and acceptable based on + specified criteria by users. The results can be visualized through + two functions: signal_quality_plot1 and + signal_quality_plot2.

+ +

An example of a signal quality plot, achievable by + setting a few thresholds. Each bar corresponds to a device, and + the y-axis shows the percentage of poor, acceptable, and missing + data in each of them.

+ +
+

For visualization across the aggregated dataset, the group module + offers violin_plot, + box_plot, radar_plot, + hist_plot, and + matrix_plot.

+ +

An example of the radar plot at the group level, + illustrating a comparison between a criterion device and a + specified device for pnni_50, + rmssd, sdnss (all + distinct extracted features for HRV), and mean heart rate + (mean_hr). The user can easily switch + between devices and conditions by interacting with the + Device and Condition + widgets.

+ +
+

Finally, WearableHRV allows for detailed + comparison between each condition and device against the criterion, + using the most commonly used statistical analyses: mean absolute + percentage error, regression analysis, ICC, and Bland-Altman + analysis through the mape_analysis, + regression_analysis, + icc_analysis, and + blandaltman_analysis functions. These + analyses are complemented with intuitive plots that establish the + validity of the wearables.

+
+
+ + Acknowledgements +

This study is an output of the ALIVE project (Improving Adolescent + mentaL health by reducing the Impact of PoVErty). This work was + supported by the Wellcome Trust [221940/Z/20/Z]. We acknowledge + contribution from Dr. Denise J. van der Mee and advice from Dr. Marco + Altini.

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