Journal article
vital_sqi: A Python package for physiological signal quality control
- Abstract:
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Electrocardiogram (ECG) and photoplethysmogram (PPG) are commonly used to determine the vital signs of heart rate, respiratory rate, and oxygen saturation in patient monitoring. In addition to simple observation of those summarized indexes, waveform signals can be analyzed to provide deeper insights into disease pathophysiology and support clinical decisions. Such data, generated from continuous patient monitoring from both conventional bedside and low-cost wearable monitors, are increasingly accessible. However, the recorded waveforms suffer from considerable noise and artifacts and, hence, are not necessarily used prior to certain quality control (QC) measures, especially by those with limited programming experience. Various signal quality indices (SQIs) have been proposed to indicate signal quality. To facilitate and harmonize a wider usage of SQIs in practice, we present a Python package, named vital_sqi, which provides a unified interface to the state-of-the-art SQIs for ECG and PPG signals. The vital_sqi package provides with seven different peak detectors and access to more than 70 SQIs by using different settings. The vital_sqi package is designed with pipelines and graphical user interfaces to enable users of various programming fluency to use the package. Multiple SQI extraction pipelines can take the PPG and ECG waveforms and generate a bespoke SQI table. As these SQI scores represent the signal features, they can be input in any quality classifier. The package provides functions to build simple rule-based decision systems for signal segment quality classification using user-defined SQI thresholds. An experiment with a carefully annotated PPG dataset suggests thresholds for relevant PPG SQIs.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 2.3MB, Terms of use)
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- Publisher copy:
- 10.3389/fphys.2022.1020458
Authors
Contributors
- Funder identifier:
- https://ror.org/029chgv08
- Grant:
- 215010/Z/18/Z
- Publisher:
- Frontiers Media
- Journal:
- Frontiers in Physiology More from this journal
- Volume:
- 13
- Article number:
- 1020458
- Publication date:
- 2022-11-11
- Acceptance date:
- 2022-10-07
- DOI:
- EISSN:
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1664-042X
- ISSN:
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1664-042X
- Pmid:
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36439252
- Language:
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English
- Keywords:
- Pubs id:
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1314692
- UUID:
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uuid_67ef9c28-af6d-4ab4-ad8e-2ed5332f50eb
- Local pid:
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pubs:1314692
- Deposit date:
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2023-02-03
- ARK identifier:
Terms of use
- Copyright holder:
- Le et al.
- Copyright date:
- 2022
- Rights statement:
- © 2022 Le, Ho, Karolcik, Hernandez, Greeff, Nguyen, Phan, Le, Thwaites, Georgiou and Clifton. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- Licence:
- CC Attribution (CC BY)
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