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Slope filtered pointwise correlation dimension algorithm and its evaluation with prefibrillation heart rate data
1AngeMed, Plymouth, Minnesota 55447.
Insights
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.
- 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.
Abstract:
Various studies have shown that a low variability in heart rate is associated with increased risk of ventricular fibrillation. Low chaotic (correlation) dimension in the heart rate also appears to predict fibrillation risk. However, these results have been based on intergroup comparisons and have not been found useful for predicting when a patient may fibrillate with any degree of sensitivity, specificity, or temporal accuracy. There are two primary limitations in using dimensional analysis to predict imminent fibrillation. The first is that the standard algorithms (for correlation dimension) assume stationarity of the system. The second limitation is that these algorithms require 10,000-50,000 data points to achieve good accuracy. Thus, even if stationarity were not an issue, there would be a lag of 2.4-12 hours to warn of impending fibrillation. An algorithm has been developed to calculate an accurate pointwise correlation dimension of heart rate data. The slope filtered pointwise correlation dimension algorithm requires as few as 1,000 points of data. Using this algorithm, it was found that the correlation dimension dropped from 2.50 +/- 0.81 to 1.07 +/- 0.18 in the minute before fibrillation in conscious pigs with an occluded coronary artery. In clinical studies, Holter tapes from patients that had suffered fatal fibrillation were also analyzed along with healthy controls and nonfibrillation ventricular patients. The fibrillation patients all had excursions of low dimension (less than 1.5), while the majority of the others did not. In the minutes before fibrillation, the correlation dimension dropped to a steady range of 0.8-1.3. Drops in the slope filtered pointwise correlation dimension appear to predict fibrillation in animals and patients.
