Key result
An automatic classification algorithm for Poincare plots using Modified Hausdorff Distance and support vector machine achieved a classification accuracy of 90.2% on MIT-BIH ECG database arrhythmia data.
Why the study?
Research on automatic classification of Poincare plots is still in its early stage, limiting their clinical application in diagnosing arrhythmias.
A novel algorithm using Modified Hausdorff Distance and support vector machine achieved 90.2% accuracy in classifying Poincare plots for arrhythmia diagnosis.
May assist arrhythmia screening as auxiliary tool; leaves open prospective clinical validation.
The Poincare plot is a method to reflect heart rate variability, which has its unique advantages in the diagnosis of arrhythmias. At present, the research on automatic classification of Poincare plots is still in its early stage, which greatly affects the clinical application of Poincare plots. Therefore, a new algorithm is proposed in this paper, which makes comprehensive use of the Modified Hausdorff Distance and support vector machine method to do multiple classification of Poincare plots. Then the arrhythmia data in MIT-BIH ECG database were used to test the algorithm. The results showed that the classification accuracy was 90.2%, indicating that the algorithm was basically reliable and could play a certain auxiliary role in clinical Poincare plots based diagnosis classification.
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Huang et al. (2019) studied Arrhythmias. Automatic classification algorithm based on Modified Hausdorff Distance and support vector machine was evaluated on Classification accuracy. An automatic classification algorithm for Poincare plots using Modified Hausdorff Distance and support vector machine achieved a classification accuracy of 90.2% on MIT-BIH ECG database arrhythmia data.
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