Artificial Neural Network Based Automatic Cardiac Abnormalities Classification
ICCIMA '05 Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications
Consistency of Random Forests and Other Averaging Classifiers
The Journal of Machine Learning Research
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Heart Rate Variability HRV analysis is a non-invasive tool for assessing the autonomic nervous system and for arrhythmia detection and classification. This paper presents a Random Forest classifier based diagnostic system for detecting cardiac arrhythmias using ECG data. The authors use features extracted from ECG signals using HRV analysis and DWT for classification. The experimental results indicate that a prediction accuracy of more than 98% can be obtained using the proposed method. This system can be further improved and fine-tuned for practical applications.