1. Mozaffarian D, Benjamin E J, Go A S, et al. Heart disease and stroke statistics-2016 update a report from the American heart association. Circulation, 2016, 133(4): e38-e360.
[2]
2. Link M S, Berkow L C, Kudenchuk P J, et al. Part 7: adult advanced cardiovascular Life support. Circulation, 2015, 132(18 suppl 2): 444-464.
[3]
3. Jekova I, Krasteva V. Real time detection of ventricular fibrillation and tachycardia. Physiol Meas, 2004, 25(5): 1167-1178.
[4]
4. Clayton R H, Murray A, Campbell R W. Recognition of ventricular fibrillation using neural networks. Med Biol Eng Comput, 1994, 32(2): 217-220.
[5]
5. Chen S, Thakor N V, Mower M M. Ventricular fibrillation detection by a regression test on the autocorrelation function. Med Biol Eng Comput, 1987, 25(3): 241-249.
[6]
6. Amann A, Tratnig R, Unterkofler K. Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators. Biomed Eng Online, 2005, 4(1): 60.
[7]
7. Clayton R H, Murray A, Campbell R W. Comparison of four techniques for recognition of ventricular fibrillation from the surface ECG. Med Biol Eng Comput, 1993, 31(2): 111-117.
[8]
8. Jekova I. Comparison of five algorithms for the detection of ventricular fibrillation from the surface ECG. Physiol Meas, 2000, 21(4): 429-439.
[9]
9. Goldberger A L, Amaral L A, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): E215-E220.
[10]
10. Li Q, Mark R G, Clifford G D. Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. Physiol Meas, 2008, 29(1): 15-32.
[11]
11. Kuo S, Dillman R. Computer detection of ventricular fibrillation. Comput Cardiol, 1978: 347-349.
[12]
12. Barro S, Ruiz R, Cabello D, et al. Algorithmic sequential decision-making in the frequency domain for Life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system. J Biomed Eng, 1989, 11(4): 320-328.
[13]
13. Jekova I. Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set. Biomed Signal Process Control, 2007, 2(1): 25-33.
[14]
14. Zhang X S, Zhu Y S, Thakor N V, et al. Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Trans Biomed Eng, 1999, 46(5): 548-555.
[15]
15. Thakor N V, Zhu Y S, Pan K Y. Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm. IEEE Trans Biomed Eng, 1990, 37(9): 837-843.
[16]
16. Amann A, Tratnig R, Unterkofler K. Detecting ventricular fibrillation by time-delay methods. IEEE Trans Biomed Eng, 2007, 54(1): 174-177.
[17]
17. Torres M E, Gamero L G, D’attellis C E. Detection of changes in nonlinear dynamical systems using multiresolution entropy. INRIA, 1996.
[18]
18. Li Qiao, Rajagopalan C, Clifford G D. Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans Biomed Eng, 2014, 61(6): 1607-1613.
[19]
19. Alonso-Atienza F, Morgado E, Fernández-Martínez L, et al. Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans Biomed Eng, 2014, 61(3): 832-840.
[20]
20. Figuera C, Irusta U, Morgado E, et al. Machine learning techniques for the detection of shockable rhythms in automated external defibrillators. PLoS One, 2016, 11(7): e0159654.
