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Statistical discriminant analysis of arrhythmias using intracardial electrograms
T R Turner1, P J Thomson, M A Cameron
1Department of Mathematics and Statistics, University of New Brunswick, Fredericton, Canada.
IEEE Transactions on Bio-Medical Engineering
|September 1, 1993
Summary
Classifying ventricular arrhythmias using intracardial electrograms is improved with statistical discrimination. A parametric model for pulse shape offers efficient and promising results for arrhythmia detection.
Area of Science:
- Cardiology
- Biomedical Engineering
- Statistical Analysis
Background:
- Ventricular arrhythmias pose a significant diagnostic challenge.
- Intracardial electrograms provide crucial data for arrhythmia analysis.
- Existing classification methods require refinement for accuracy and efficiency.
Purpose of the Study:
- To develop and evaluate a novel statistical approach for classifying ventricular arrhythmias.
- To utilize a parametric model for intracardial electrogram pulse shape analysis.
- To assess the efficacy of standard discrimination procedures in this context.
Main Methods:
- Application of standard statistical discrimination procedures.
- Development of a simple parametric model for the electrogram pulse peak.
- Utilizing both linear and quadratic discrimination functions for analysis.
Main Results:
- The proposed method effectively utilizes model parameters for classification.
- The approach exhibits well-established statistical properties.
- Preliminary analyses on real data demonstrate promising classification accuracy.
Conclusions:
- Statistical discrimination with a parametric pulse model is a viable method for ventricular arrhythmia classification.
- The computational efficiency of this approach makes it suitable for clinical application.
- Further validation on extensive datasets is warranted to confirm its diagnostic utility.