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Related Experiment Videos

Slope filtered pointwise correlation dimension algorithm and its evaluation with prefibrillation heart rate data.

M W Kroll1, K W Fulton

  • 1AngeMed, Plymouth, Minnesota 55447.

Journal of Electrocardiology
|January 1, 1992
PubMed
Summary
This summary is machine-generated.

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Low heart rate variability and chaotic dimension predict fibrillation risk. A new algorithm accurately predicts impending fibrillation by detecting drops in correlation dimension minutes before the event.

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Nonlinear Dynamics

Background:

  • Low heart rate variability is linked to increased risk of ventricular fibrillation.
  • Previous dimensional analysis methods for predicting fibrillation have limitations, including assumptions of stationarity and large data requirements, leading to poor predictive accuracy and temporal resolution.

Purpose of the Study:

  • To develop and validate a novel algorithm for accurately calculating the pointwise correlation dimension of heart rate data.
  • To assess the utility of this new algorithm in predicting imminent ventricular fibrillation in animal models and human patients.

Main Methods:

  • Development of a slope-filtered pointwise correlation dimension algorithm requiring fewer data points (as few as 1,000).
  • Application of the algorithm to heart rate data from conscious pigs with occluded coronary arteries prior to fibrillation.

Related Experiment Videos

  • Analysis of Holter tape recordings from human patients who experienced fatal fibrillation, alongside healthy controls and non-fibrillating ventricular patients.

    • Main Results:

    • In pigs, the correlation dimension decreased significantly from 2.50 +/- 0.81 to 1.07 +/- 0.18 in the minute preceding fibrillation.
    • Patients who experienced fibrillation consistently showed excursions of low correlation dimension (<1.5), unlike control groups.
    • In the minutes before fibrillation, the correlation dimension dropped to a stable range of 0.8-1.3.

    Conclusions:

    • Drops in the slope-filtered pointwise correlation dimension effectively predict impending ventricular fibrillation in both animal models and human patients.
    • This novel algorithm offers improved sensitivity, specificity, and temporal accuracy for fibrillation prediction compared to previous methods.