Key result
An ensemble classifier of neural networks based on Poincaré plot geometric patterns demonstrated better performance than SVM classifiers for automatically recognizing cardiac arrhythmias.
Why the study?
Does an ensemble neural network classifier improve the automatic recognition of cardiac arrhythmias from Poincaré plots compared to SVM classifiers in patients undergoing 24h ECG monitoring?
Does an ensemble neural network classifier improve the automatic recognition of cardiac arrhythmias from Poincaré plots compared to SVM classifiers in patients undergoing 24h ECG monitoring?
An ensemble neural network classifier effectively automates the recognition of cardiac arrhythmias using geometric patterns of Poincaré plots from 24-hour ECG recordings, outperforming standard SVM classifiers.
May enhance automated arrhythmia detection from ECG; leaves open prospective clinical validation.
The Poincaré plot emerges as an effective tool for assessing cardiovascular autonomic regulation. It displays nonlinear characteristics of heart rate variability (HRV) from electrocardiographic (ECG) recordings and gives a global view of the long range of ECG signals. In the telemedicine or computer-aided diagnosis system, it would offer significant auxiliary information for diagnosis if the patterns of the Poincaré plots can be automatically classified. Therefore, we developed an automatic classification system to distinguish five geometric patterns of the Poincaré plots from four types of cardiac arrhythmias. The statistics features are designed on measurements and an ensemble classifier of three types of neural networks is proposed. Aiming at the difficulty to set a proper threshold for classifying the multiple categories, the threshold selection strategy is analyzed. 24 h ECG monitoring recordings from 674 patients, which have four types of cardiac arrhythmias, are adopted for recognition. For comparison, Support Vector Machine (SVM) classifiers with linear and Gaussian kernels are also applied. The experiment results demonstrate the effectiveness of the extracted features and the better performance of the designed classifier. Our study can be applied to diagnose the corresponding sinus rhythm and arrhythmia substrates disease automatically in the telemedicine and computer-aided diagnosis system.
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Zhang et al. (2015) studied Cardiac arrhythmias (n=674). Ensemble classifier of neural networks based on Poincaré plot geometric patterns vs. Support Vector Machine (SVM) classifiers with linear and Gaussian kernels was evaluated on Classification of five geometric patterns of Poincaré plots from four types of cardiac arrhythmias. An ensemble classifier of neural networks based on Poincaré plot geometric patterns demonstrated better performance than SVM classifiers for automatically recognizing cardiac arrhythmias.
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