References
- Reul HM, Akdis M. Blood pumps for circulatory support. Perfusion. 2000;15:295–311.
- Saito S, Westaby S, Piggot D, et al. End-organ function during chronic nonpulsatile circulation. Ann Thorac Surg. 2002;74:1080–1085.
- Garatti A, Bruschi G, Colombo T, et al. Clinical outcome and bridge to transplant rate of left ventricular assist device recipient patients: comparison between continuous-flow and pulsatile-flow devices. Eur J Cardio-Thorac Surg. 2008;34:275–280.
- Sandner SE, Zimpfer D, Zrunek P, et al. Renal flow function after implantation of continuous versus pulsatile left ventricular assist devices. J Heart Lung Transplant. 2008;27:469–473.
- Radovancevic B, Vrtovec B, de Kort E, et al. End-organ function in patients on long-term circulatory support with continuous- or Pulsatile-flow assist devices. J Heart Lung Transplant. 2007;26:815–818.
- Kamdar F, Boyle A, Liao K, et al. Effects of centrifugal, axial, and pulsatile left ventricular assist device support on end-organ function in heart failure patients. J Heart Lung Transplant. 2009;28:352–359.
- Chow G, Roberts IG, Edwards AD, et al. The relation between pump flow rate and pulsatility on cerebral hemodynamics during pediatric cardiopulmonary bypass. J Thorac Cardiovasc Surg. 1997;114:568–577.
- Sezai A, Shiono M, Orime Y, et al. Major organ function under mechanical support: comparative studies of pulsatile and nonpulsatile circulation. Artif Organs. 1999;23:280–285.
- O’Neil MP, Fleming JC, Badhwar A, et al. Pulsatile versus nonpulsatile flow during cardiopulmonary bypass: microcirculatory and systemic effects. Ann Thorac Surg. 2012;94:2046–2053.
- Cheng A, Williamitis CA, Slaughter MS. Comparison of continuous-flow and pulsatile-flow left ventricular assist devices: is there an advantage to pulsatility? Ann Cardiothorac Surg. 2014;3:573–581.
- Ai Y, Pan B, Fu Y, et al. Control system design for a novel minimally invasive surgical robot. Comput Assisted Surg. 2016;21:45–53.
- Niu G, Pan B, Ai Y, et al. Intuitive control algorithm of a novel minimally invasive surgical robot. Comput Assisted Surg. 2016;21:92–101.
- Israel SA, Irvine JM, Cheng A, et al. ECG to identify individuals. Pattern Recognit. 2005;38:133–142.
- Singh YN, Gupta P. ECG to Individual Identification. 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems; 2008.
- Yongjin W, Plataniotis KN, Hatzinakos D. Integrating analytic and appearance attributes for human identification from ECG signals. Paper presented at Biometric Consort Conference IEEE; 2006.
- Li G, Zhang S, Yang L, et al. Computerized analysis of acceleration parameter for the non-stress test normal and potentially abnormal fetuses. Comput Assisted Surg. 2016;21:1–5.
- Karpagachelvi S, Arthanari M, Sivakumar M. ECG feature extraction techniques: a survey approach. Physics. 2010;8:76-80.
- Chen HC, Chen SW. A moving average based filtering system with its application to real-time QRS detection. Paper presented at Computers in Cardiology; 2004.
- Li C, Zheng C, Tai C. Detection of ECG characteristic points using wavelet transforms. IEEE Trans Biomed Eng. 1995;42:21
- Pal S, Mitra M. Detection of ECG characteristic points using Multiresolution Wavelet Analysis based selective coefficient method. Measurement. 2010;43:255–261.
- Mallat S, Zhong S. Characterization of signals from multiscale edges. IEEE Trans Pattern Anal Machine Intell. 1992;14:710–732.
- Yan S, Chan KL, Krishnan SM. Characteristic wave detection in ECG signal using morphological transform. BMC Cardiovascu Disord. 2005;5:1–7.
- Pandit D, Zhang L, Liu C, et al. A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm. Comput Methods Programs Biomed. 2017;144:61–75.
- Zhang H, Li R-j, Huang X, et al. Activities of the sinus node pacemaking during the simulated atrial reentry. Comput Assisted Surg. 2016;21:11–16.
- Goldberger AL, Amaral LA, Glass L, et al. Physiobank, physiotoolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101:E215–ee20.
- MIT-BIH Arrhythmia Database [Internet]. Cambridge: Harvard-MIT Division of Health Sciences and Technology Biomedical Engineering Center; 2017 [cited 2017 Sep 18]. Available from: https://physionet.org/physiobank/database/mitdb/
- Lim HW, Hau YW, Lim CW, et al. Artificial intelligence classification methods of atrial fibrillation with implementation technology. Comput Assisted Surg. 2016;21:154–161.