Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data

Abstract

Huge hospital information system databases can be mined for knowledge discovery and decision support, but artifact in stored non-invasive vital sign (VS) high-frequency data streams limits its use. We used machine-learning (ML) algorithms trained on expert-labeled VS data streams to automatically classify VS alerts as real or artifact, thereby “cleaning” such data for future modeling. 634 admissions to a step-down unit had recorded continuous noninvasive VS monitoring data [heart rate (HR), respiratory rate (RR), peripheral arterial oxygen saturation (SpO2) at 1/20 Hz, and noninvasive oscillometric blood pressure (BP)]. Time data were across stability thresholds defined VS event epochs. Data were divided Block 1 as the ML training/cross-validation set and Block 2 the test set. Expert clinicians annotated Block 1 events as perceived real or artifact. After feature extraction, ML algorithms were trained to create and validate models automatically classifying events as real or artifact. The models were then tested on Block 2. Block 1 yielded 812 VS events, with 214 (26 %) judged by experts as artifact (RR 43 %, SpO2 40 %, BP 15 %, HR 2 %). ML algorithms applied to the Block 1 training/cross-validation set (tenfold cross-validation) gave area under the curve (AUC) scores of 0.97 RR, 0.91 BP and 0.76 SpO2. Performance when applied to Block 2 test data was AUC 0.94 RR, 0.84 BP and 0.72 SpO2. ML-defined algorithms applied to archived multi-signal continuous VS monitoring data allowed accurate automated classification of VS alerts as real or artifact, and could support data mining for future model building.

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Funding

This study was funded with grant support from the United States National Institutes of Health, National Institute of Nursing Research RO1 NR13912 and the National Science Foundation NSF 1320347. The funding bodies approved the study design as submitted in the grant application proposal, but had no role in the data collection, analyses, or interpretation, manuscript preparation, or decision to submit the manuscript for publication.

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Correspondence to Marilyn Hravnak.

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The study was approved and has current active approval from the University of Pittsburgh Institutional Review Board (PRO12070002). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required, and was approved with consent waiver.

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The authors have no commercial conflicts of interest to report.

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Hravnak, M., Chen, L., Dubrawski, A. et al. Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data. J Clin Monit Comput 30, 875–888 (2016). https://doi.org/10.1007/s10877-015-9788-2

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Keywords

  • Physiologic monitoring
  • Machine learning
  • Archived data
  • Big-data
  • Vital signs
  • Artifact
  • Cardiorespiratory instability