Rohan Baid , Niranjana Krupa , Muhammad A.M. Ali


  1. [1] J. Allen and A. Murray, Age-related changes inperipheral pulse timing characteristic at the ears, fingersand toes, Journal of Human Hypertension 16, 2002, 711-717.
  2. [2] V. S. Murthy, S. Ramamoorthy, N. Srinivasan S.Rajagopal and M. M. Rao, Analysis ofphotoplethysmographic signals of cardiovascular patients,Proc. of the 23rd Annu EMBS Int Conf., 2001, pp. 2204-2207.
  3. [3] X. Xiao, M. R. Kaazempur-Mofrad, E. T. Ozawa andR. D. Kamm, Non- invasive assessment of cardiovascularhealth. A computational/experimental study, ASMEBioengineering Conf., 2001, pp.67-68.
  4. [4] K. Takazawa, N. Tanaka, M. Fujita and O.Matsuoka, Assessment of vasoactive agents and vascularaging by the second derivative of photoplethysmogramwaveform, Hypertension vol 32 pp.365-370, (AmericanHeart Association, 7272 Greenville, 1998).
  5. [5] Nastaran Hesam Shariati, Parametric Modeling andclassification of the photoplethysmographic Signal-Thesis, University Kebangsaan Malaysia Bangi, 2006.
  6. [6] G. Madhavan, Plethysmography, BiomedicalInstrumentation & Technology, vol 39, 2005, pp. 367-371.
  7. [7] D. Barschdorff and W. Zhang, Respiratory RhythmDetection with photoplethysmographic methods, Proc. of10th Annu. Int IEEE Conf. in Medicine and Biology,1994, pp. 912-913.
  8. [8] S. C. Millasseau, R. P. Kelly, J. M. Ritter and P. J.Chowienczyk, Determination of age-related increases inlarge artery stiffness by digital pulse contour analysis,Clinical science, vol 103, 2002, pp. 371-377.
  9. [9] M. Bolanos, H. Nazeran and E. Haltiwanger,,Comparison of heart rate variability signal featuresderived from electrocardiography andphotoplethysmography in healthy individual, Proc. of the28th IEEE EMBS Annu. Int Conf., New York City, USA,Aug 30-Sept 3, 2006.
  10. [10] Edmond Zahedi, Mohd Alauddin Mohd Ali,Parametric differential approach for modeling the upperlimb human vasculature, Proc. of the 26th Annu. Int Conf.of the IEEE EMBS, San Francisco, USA, September 1-5,2004.
  11. [11] Simon Haykin, NEURAL NETWORKS- AComprehensive Foundation, 2nd edition (Chapter 6),(Pearson Education, Inc, 1999).
  12. [12] Y. U. Chenggang, L. IU. Zhenqiu, Thomas M C KEnna, Andrew T. R Eisner and J. Aques R Eifman, AMethod for automatic identification of reliable heart ratescalculated from ECG and PPG waveforms, Journal of theAmerican Medical Informatics Association vol 13, 2006.
  13. [13] Eun-Hye Jang, Byoung-Jun Park, Sang-Hyeob Kimand Jin-Hun Sohn, Emotion classification by machinelearning algorithm using physiological signals, IACSITHong Kong Conf. IPCSIT, vol. 25, Singapore, 2012.186
  14. [14] F. Mokhayeri, M-R. Akbarzade h-T, S. Toosizadeh,Mental stress detection using physiological signals basedon soft computing techniques, 18th Iranian Conf. onBioMedical Engineering, Tehran, Iran, December 14-16,2011.
  15. [15] Jerzy Wtorek, Adam Bujnowski, Jacek Rumiński,Artur Poliński, Mariusz Kaczmarek, Antoni Nowakowski.Assessment of cardiovascular risk in assisted living,Metrol. Meas. Syst., 19(2), 2012, pp. 231-244.
  16. [16] L. Ljung, System identification theory for the user,(Upper Saddle River, NJ: Prentice Hall, 1999).
  17. [17] Rohan Baid, Basavaraju S., B. Niranjana Krupa andM. A. Mohd Ali, A comparison of linear parametricmodels based on the fitness index, Proc. of the NationalConf on Evolutionary Systems for Signal Processing andCommunication (ESSC), 2012, pp. 1-5.
  18. [18] Davide Mattera, Francesco Palmieri, and SimonHaykin, An explicit algorithm for training support vectormachines, IEEE Signal Processing Letters, vol 6, 1999,pp. 243-245.

Important Links:

Go Back