Abstract
Application of impedance plethysmography (IP) for impedance measurement is the paradigm in assessment of central and peripheral blood flow. We propose the expediency of IP to unearth hidden patterns from Plethysmographic observations on a radial pulse. The variability analysis in one thousand control and disease subjects evolves an archetype of eight different morphological patterns. The peripheral pulse waveforms not only characterize the physiology of control subjects, but also define the morphology of patients suffering from Myocardial Infarction, cirrhosis of liver, and disorder of lungs. Diverse parallel support vector machine (pSVM) topologies are designed as an aid to the physician for multiclass pattern recognition problem. Besides a lowest confusion coefficient (0.133), the PCA-based pSVM classifier offers a comparatively higher generalized correlation coefficient and κ value of 0.6586 and 0.8407, respectively. However, the ROC characteristics and the benchmark parameters suggest that wavelet-based pSVM is the optimum classifier with a sensitivity of 85.33 %, an elevated MCC (0.69), and a least upper bound on the expected error. pSVM stands out as a model classifier as compared with identified indices such as, Fishers Ratio, Morphology Index, and Heart rate variability.
Similar content being viewed by others
References
Abhinav, S.M., Kumar, M., Anand, S., Salhan, A., Santhosh, J.: Nadi Yantra: a robust system design to capture the signals from the radial artery for non-invasive diagnosis. The IEEE 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE 2008), Shanghai, China, May 16–18, pp. 1387–1390 (2008)
Jeon, Y.J., Kim, J.U., Lee, H.J., et al.: A clinical study of the pulse wave characteristics at the three pulse diagnosis positions of Chon, Gwan and Cheok. Evid-based. Complement. Altern. Med. 2011 (2011). doi:10.1093/ecam/nep150
Yoon Y., Lee M., Soh K.: Pulse type classification by varying contact pressure. IEEE Eng. Med. Biol. Mag. 19, 106–110 (2000)
Tartiere J.M., Logeart D., Beauvais F., Chavelas C., Kesri L., Tabet J.Y., Solal A.C.: Non-invasive radial pulse wave assessment for the evaluation of left ventricular systolic performance in heart failure. Eur. J. Heart Fail. 9, 477–483 (2007)
Peripheral Pulse Analyzer, Internet: Available from:http://www.larsentoubro.com/lntcorporate/uploads/product/Nivomon.pdf
Padilla J.M., Berjano E.J., Siz J., Fcila L., Daz P., Merc S.: Assessment of relationships between blood pressure, pulse wave velocity and digital volume pulse. Comput. Cardiol. 33, 893–896 (2006)
Millaseau S.C., Guigui F.G., Kelly R.P.: Non-invasive assessment of the digital volume pulse: comparison with the peripheral pressure pulse. Hypertension 36, 952–956 (2000)
Jayasree V.K., Shaija P.J., Manu P.J., Radhakrishnan P.: An optoelectronic sensor configuration for the determination of age related indices using blood volume pulse. Sens. Transducers J. 87(1), 39–45 (2008)
Lau, O., Chwang, A.: Relationship between wrist pulse characteristics and body conditions. In: Proceedings of the 14th Engineering Mechanics Conference (EM2000), University of Texas, Austin, May 21–25 (2000)
Golden J.C., Miles D.S.: Assessment of peripheral haemodynamic using impedance plethysmography. Phys. Ther. 66(10), 1544–1547 (1986)
Webster J., Ravi Shankar T.M., Shao S.Y.: The contribution of vessel volume change and blood resistivity change to the electrical impedance pulse. IEEE Trans. Biomed. Eng. BME-32(3), 192–198 (1985)
Annanthakrishnan T.S., Jindal G.D., Kataria S.K., Ananthakrishnan T.S., Jindal G.D., Sinha V., Jain R.K., Kataria S.K., Deshpande A.K.: Clinical validation of software for a versatile variability analyzer: assessment of autonomic function. J. Med. Phys. 32(3), 97–102 (2007)
Kubicek W.G., Kottke E.J., Ramos F.J., Patterson R.P., Witsoe D.A., Labree J.W., Remole W.: The Minnesota impedance cardiograph theory and applications. Biomed. Eng. 9, 410–416 (1974)
Vedru J.: Electrical impedance methods for the measurement of stroke volume in man: state of the art. Acta et Comm Univ Tartuensis 974, 110–129 (1994)
Jagruti, C., Chaugule, N., Ananthakrishnan, T.S., Babu, J.P., Jindal, G.D.: Normalized dZ/dt waveform for easy assessment of peripheral blood flow. In: Proceedings of SBME-NM 2000, Bhabha Atomic Research Centre, pp. 67–73 (2000)
Mohapatra S.N., Helena A.M.: Measurement of peripheral blood flow by electrical impedance technique. J. Med. Eng. Tech. 3(3), 132–137 (1979)
Brown, B.H., Pryce, W.I.J., Clarke, R.G.: Impedance plethysmography: can it measure changes in limb blood flow. Med. Biol. Eng. 13, 674–681 (1975)
Yamamoto Y., Yamamoto T., Oberg P.A.: Impedance plethysmography for blood flow measurement in limbs. Med. Biol. Eng. Comput. 30, 518–524 (1992)
Karamchandani, S., Desai, U.B., Merchant, S.N., Jindal, G.D.: Principal component analysis based backpropagation algorithm for diagnosis of peripheral arterial occlusive diseases. IEEE 22nd Canadian Conference Electrical and Computer Engineering, (CCECE), Newfoundland, Canada, May 3–6, pp. 482–485 (2009)
Sakamoto K., Mute K., Kanai H.: Problems of impedance cardiography. Med. Biol. Eng. Comput. 17, 697–709 (1979)
Shin H.S., Lee C., Lee M.: Adaptive threshold method for the peak detection of photo plethysmographic waveform. Comput. Biol. Med. 39(12), 1145–1152 (2009)
Ahdesmaki, M., Lahdesmaki, H., Yli-Harja, O.: Roubust Fishers test for periodicity detection in noisy biological time series. In: Proceedings of IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS 07), Tuusula, Finland, (June 2007)
Nawsupe, G., Gadre, V., Joshi, R.R.: Pulse signal analysis and characterization. Mtech Thesis, School of BioSciences and Bio Engineering, Indian Institute Of Technology, Mumbai, India, pp. 44 (2008)
Jain, R.K., Jindal, G.D., Ananthakrishnan, T.S.: Central and peripheral pulse morphology for disease characterization. National Symposium on Nuclear Instrumentation (NSNI), Feb. 24–26, Bhabha Atomic Research Centre, India (2010)
Malik M.: Standards of measurement, physiological interpretation, and clinical use. Eur. Heart J. 17(3), 354–381 (1996)
Akselrod S., Gordon D., Ubel F.A., Shannon D.C., Barger A.C., Cohen R.J.: Power spectrum analysis of heart rate fluctuations: a quantitative probe of beat-to-beat cardiovascular control. Science 213, 220–222 (1981)
Singh B.N., Tiwari A.K.: Optimal selection of wavelet basis function applied to ECG signal denoising. Digit. Signal Process. 16, 275–287 (2006)
Dogaru, R.: Fast and efficient speech signal classification with a novel nonlinear transform. In: Proceedings of International Symposium on Information Technology Convergence, pp. 43–47 (2007)
Sharma L.N., Dandapat S., Mahanta A.: ECG signal denoising using higher order statistics in Wavelet subbands. Biomed. Signal Process. Control 5, 214–222 (2010)
Hsu C., Lin C.: A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Netw. 13, 415–425 (2002)
Ivanciuc O.: Applications of support vector machines in chemistry. Rev. Comput. Chem. 23, 291–400 (2007)
Anguita, D., Ridella, S., Sterpi, D.: A new method for multiclass support vector machines. In: Proceedings of IEEE International Joint Conference Neural Networks vol. 1, pp. 407–412 (2004)
Liu Y., You Z., Cao L.: A novel and quick SVM-based multi-class classifier. Pattern Recognit. 39, 2258–2264 (2006)
Chapelle O., Vapnik V., Bousquet O., Mukherjee S.: Choosing multiple parameters for support vector machines. Mach. Learn. 46, 131–159 (2002)
Watkins M.W., Pacheco M.: Interobserver agreement in behavioral research: importance and calculation. J. Behav. Educ. 10(4), 205–212 (2000)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Karamchandani, S.H., Desai, U.B., Merchant, S.N. et al. Parallel support vector architectures for taxonomy of radial pulse morphology. SIViP 7, 975–990 (2013). https://doi.org/10.1007/s11760-012-0287-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-012-0287-3