Prediction of arrhythmia from MIT-BIH database using random forest (RF) and voted perceptron (VP) classifiers

Author:

Vinutha K.,Thirunavukkarasu Usharani

Publisher

AIP Publishing

Reference53 articles.

1. R. G. Kumar, Y. S. Kumaraswamy, and Others, “Investigating cardiac arrhythmia in ECG using random forest classification,” Int. J. Comput. Appl., vol. 37, no. 4, pp. 31–34, 2012.

2. A. J. Shah et al., “Body Surface Electrocardiographic Mapping for Non-invasive Identification of Arrhythmic Sources,” ArrhythmElectrophysiol Rev, vol. 2, no. 1, pp. 16–22, Apr. 2013.

3. M. H. Sarwar, A. R. Mughal, S. Mughal, and M. Sarwar, “Concerns of Heart Diseases and Mediations to Encourage Healthful Actions for Their Deterrence,” International Journal of Bioinformatics and Biomedical Engineering, vol. 1, no. 2, pp. 70–76, 2015.

4. Clinical Outcome after Radiofrequency Catheter Ablation of Focal Atrial Fibrillation Triggers

5. J. Park, S. Lee, and K. Kang, “Arrhythmia detection using amplitude difference features based on random forest,” Conf. Proc. IEEE Eng. Med. Biol. Soc., vol. 2015, pp. 5191–5194, 2015.

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