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J Clin Monit Comput. 2016 Dec;30(6):875-888. Epub 2015 Oct 5.

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

Author information

1
Department of Acute and Tertiary Care, University of Pittsburgh Schools of Nursing, 336 Victoria Hall; 3500 Victoria St., Pittsburgh, PA, 15261, USA. mhra@pitt.edu.
2
Carnegie Mellon University Robotics Institute (Auton Lab), Pittsburgh, PA, USA.
3
Department of Acute and Tertiary Care, University of Pittsburgh Schools of Nursing, 336 Victoria Hall; 3500 Victoria St., Pittsburgh, PA, 15261, USA.
4
Department of Critical Care Medicine, University of Pittsburgh Schools of Medicine, Pittsburgh, PA, USA.

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.

KEYWORDS:

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

PMID:
26438655
PMCID:
PMC4821824
DOI:
10.1007/s10877-015-9788-2
[Indexed for MEDLINE]
Free PMC Article

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