论文信息 - Arrythmia Classification of Electrocardiogram Recorded Data with Random Forest Method - 字舞流文

Arrythmia Classification of Electrocardiogram Recorded Data with Random Forest Method

Dwi Esti Kusumandari | Arjon Turnip | Sutrisno Salomo Hutagalung | Yosafat Vincent Saragih | Jessica Tania | A. Turnip | D. E. Kusumandari | S. S. Hutagalung | Jessica Tania

[1] Wei Li,et al. Local Deep Field for Electrocardiogram Beat Classification , 2018, IEEE Sensors Journal.

[2] Che Wun Chiou,et al. Analyzing ECG for cardiac arrhythmia using cluster analysis , 2012, Expert Syst. Appl..

[3] Manal M. Tantawi,et al. Hybrid hierarchical method for electrocardiogram heartbeat classification , 2018, IET Signal Process..

[4] C. Liew,et al. Nonenzymatic separation of myocardial cell nuclei from whole heart tissue. , 1983, The American journal of physiology.

[5] Philip de Chazal,et al. Automatic classification of heartbeats using ECG morphology and heartbeat interval features , 2004, IEEE Transactions on Biomedical Engineering.

[6] Elif Derya Übeyli. ECG beats classification using multiclass support vector machines with error correcting output codes , 2007, Digit. Signal Process..

[7] K. P. Soman,et al. An Efficient R-peak Detection Based on New Nonlinear Transformation and First-Order Gaussian Differentiator , 2011 .

[8] William Robson Schwartz,et al. ECG-based heartbeat classification for arrhythmia detection: A survey , 2016, Comput. Methods Programs Biomed..

[9] David Menotti,et al. Learning Deep Off-the-Person Heart Biometrics Representations , 2017, IEEE Transactions on Information Forensics and Security.

[10] M. Y. Mashor,et al. Effects of the Window Size and Feature Extraction Approach for Arrhythmia Classification , 2017 .