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Morphograms: exploiting correlation patterns to efficiently identify clinically significant events in intensive care

Walid Ali1, Larry Eshelman

  • 1Philips Res., Briarcliff Manor, NY, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
PubMed
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This study introduces a novel method using cross-correlations between electrocardiogram (ECG) and arterial blood pressure (ABP) signals to improve ABP signal quality assessment and artifact detection. The technique shows promise in accurately identifying signal anomalies in patient monitoring data.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Medical Informatics

Background:

  • Arterial blood pressure (ABP) monitoring is crucial in critical care.
  • Signal quality assessment and artifact detection in ABP are essential for accurate clinical decision-making.
  • Existing methods may have limitations in robustness and simplicity.

Purpose of the Study:

  • To develop and validate a simple technique for assessing ABP signal quality.
  • To detect artifacts in ABP signals using cross-correlations with electrocardiogram (ECG) signals.
  • To enhance the reliability of ABP data in patient monitoring.

Main Methods:

  • Utilized cross-correlations between simultaneously recorded ECG and ABP signals.
  • Employed a physician-annotated patient monitoring signal database (Beth Israel/Harvard-MIT).

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  • Evaluated the technique's performance in classifying artifacts and true events.

    • Main Results:

    • The technique demonstrated effective artifact detection in ABP signals.
    • Achieved 45% correct classification of manually annotated artifacts.
    • Successfully classified 98% of manually annotated true events, indicating high specificity.

    Conclusions:

    • Cross-correlation analysis between ECG and ABP signals is a viable method for signal quality assessment.
    • This technique offers a simple yet effective approach to artifact detection in ABP monitoring.
    • The findings support the integration of this method into clinical practice for improved data reliability.