Author : Madhura V. Gupte 1
Date of Publication :7th July 2016
Abstract: Due to high cost of ECG signal, it is difficult to measure it regularly. In this paper we suggested a low cost, non invasive and easy methodology to analyze human cardio vascular system by using parameters of PPG as this signal is highly correlated with ECG. This paper deals with extraction of time domain features of PPG signal, correlate it with ECG and select most relevant features. The comparison of selected features and overall features has been done by Support Vector Machine (SVM) and Artificial Neural Network (ANN) and Linear Regression which is use as a predictors.
 Amal Mattu, William Brady, “ECG 'for the Emergency Physician”, BMJ Books, BMA House, Tavistock Square, London 2003.
 John R. Hampton, “The ECG Made Easy”, Elsevier Science Health Science Division, 2008.
 M. Maguire and T. Ward, “The Photoplethysmograph as an instrument for physiological measurement,” Department of Electronic Engineering, NUI Maynooth 2002.
 N. Selvaraj, J. Santosh, K.K. Dipak. S. Anand “Assesment of heart rate variability derived from finger tip photoplethysmography as compared to electrocardiography”, Journel of Medical Engineering and technology, vol 32, 2008.
 Grimaldi, D. et al., “Photoplethysmography detection by smartphone’s videocamera”, in: Proc. IEEE 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS) 2011, pp. 488-491.
 M. Bolanos, H. Nazeran, E. Haltiwanger, “Comparison of heart rate variability signal features derived from electrocardiography and photoplethysmography in healthy individuals”, in: Proc. 28th Annual Int. Conf. of the IEEE , New York City, USA, 2006.
 U. Anlinker, et .al "AMON: a wearable multiparameter medical monitoring and alert system" IEEE Trans. Inform. Technol. in Biomed., vol..8, no.4, pp.415-427, 2004.
 Lu g et al., “A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects”, Journal of Medical Engineering & Technology, vol. 33, 2009.
 Rohan Banerjee et al., “Estimation of ECG Parameters using Photoplethysmography”, IEEE, 2013.
 Elgendi M., “On the analysis of fingertip photoplethysmogram signals”, Current Cardiology Reviews, vol.8, 2012.
 Zhao, Q. et al., “ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines”, in: Proc. International Conference on Neural Networks and Brain, ICNN& B, 2005, vol. 2.
 S. C. Saxena, V. Kumar, and S. T. Hamde, “Feature extraction from ECG signals using wavelet transforms for disease diagnostics”, International Journal of Systems Science, vol. 33, no. 13, pp. 1073- 1085, 2002.
 Chouhan, S. et al., “Detection of QRS Complexes in 12 lead ECG using Adaptive Quantized Threshold”, IJCSNS International Journal of Computer Science and Network Security, 2008, vol.8.
 Reshef, D. et al., “Detecting Novel Associations in Large Data Sets”Science, 2011, vol. 334.
 http://http://luna.cas.usf.edu/˜mbrannic/files/regression/corr 1.html
 Rosaria Silipo & Carlo Marchesi, member IEEE (1998), “Artificial Neural Networks for Automatic ECG Analysis”, IEEE transactions on signal processing, vol.46, No.5.
 P. Trahanias & E.Skordalakis (1990), “Syntactic pattern recognition of the ECG,”, IEEE Trans. PatternAnal. Machine Intelligence, vol. 12, pp. 648-657.
 C.J.C. Burges. “Geometry and invariance in kernel based methods”. In B. Sch¨olkopf, C.J.C. Burges, and A.J. Smola, editors, Advances in Kernel Methods: Support Vector Learning
 Jason Weston, “Support Vector Machine and Statistical Learning Theorial Tutorial”, NEC Labs America. vol 8, 2003.
 Goldman RN, Weinberg JS. “Statistics: an introduction.” Upper Saddle River, 1985; 72-98.