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HeartView: An Extensible, Open-Source, Web-Based Signal Quality Assessment Pipeline for Ambulatory Cardiovascular Data

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Pervasive Computing Technologies for Healthcare (PH 2023)

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Abstract

Wearable sensing systems enable peripheral physiological data to be collected repeatedly in naturalistic settings. However, the ambulatory nature of wearable biosensors predisposes them to common signal artifacts that researchers must address before analysis. Signal quality assessment procedures are time-consuming and non-standardized across research teams, and transparent reporting of custom, closed-source pipelines needs improvement. This paper presents HeartView, an extensible, open-source, web-based signal quality assessment pipeline that visualizes and quantifies missing beats and invalid segments in heart rate variability (HRV) data obtained from ambulatory electrocardiograph (ECG) and photoplethysmograph (PPG) signals. We demonstrate the utility of our pipeline on two datasets: (1) 34 ECGs recorded with the Actiwave Cardio from children with and without autism, and (2) 15 sets of ECGs and PPGs recorded with the RespiBAN and Empatica E4, respectively, from healthy adults in the publicly available WESAD dataset. Our pipeline demonstrates interpretable group differences in physiological signal quality. ECGs of children with autism contain more missing beats and invalid segments than those without autism. Similarly, PPG data contains more missing beats and invalid segments than ECG data. HeartView has a graphical user interface in the form of a web-based dashboard at https://github.com/cbslneu/heartview.

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Notes

  1. 1.

    The survey sample comprised 31% researchers and 69% engineers from the Society of Psychophysiological Research (SPR), the IEEE International Machine Learning for Signal Processing (MLSP) workshop, and snowball sampling using personal contacts and social media.

  2. 2.

    As our primary intent is to introduce a tool for researchers to perform initial assessment checks in their SQA, HeartView currently includes only one of several possible algorithms for ECG beat detection [36,37,38,39]. Future users could select and implement additional or alternative state-of-the-art algorithms.

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Authors and Affiliations

  1. Khoury College of Computer Sciences and Bouvé College of Health Sciences, Northeastern University, Boston, MA, 02115, USA

    Natasha Yamane, Varun Mishra & Matthew S. Goodwin

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  1. Natasha Yamane
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  2. Varun Mishra
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  3. Matthew S. Goodwin
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Correspondence to Natasha Yamane .

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Editors and Affiliations

  1. Malmö University, Malmö, Sweden

    Dario Salvi

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Pieter Van Gorp

  3. University of Edinburgh, Edinburgh, UK

    Syed Ahmar Shah

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Yamane, N., Mishra, V., Goodwin, M.S. (2024). HeartView: An Extensible, Open-Source, Web-Based Signal Quality Assessment Pipeline for Ambulatory Cardiovascular Data. In: Salvi, D., Van Gorp, P., Shah, S.A. (eds) Pervasive Computing Technologies for Healthcare. PH 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 572. Springer, Cham. https://doi.org/10.1007/978-3-031-59717-6_8

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  • DOI: https://doi.org/10.1007/978-3-031-59717-6_8

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