Abstract
By far, a number of sensors have been employed for pulse signal acquisition, which can be grouped into three major categories, i.e., pressure, photoelectric, and ultrasonic sensors. To guide the sensor selection for computational pulse diagnosis, in this chapter, we analyze the physical meanings and sensitivities of signals acquired by these three types of sensors. The dependency and complementarity of the different sensors are discussed from both the perspective of cardiovascular fluid dynamics and comparative experiments by evaluating disease classification performance. Experimental results indicate that each sensor is more appropriate for the diagnosis of some specific disease that the changes of physiological factors can be effectively reflected by the sensor, e.g., ultrasonic sensor for diabetes and pressure sensor for arteriosclerosis, and improved diagnosis performance can be obtained by combining three types of signals.
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References
S. Walsh and E. King, Pulse Diagnosis: A Clinical Guide. Sydney Australia: Elsevier, 2008.
V. D. Lad, Secrets of the Pulse. Albuquerque, New Mexico: The Ayurvedic Press, 1996.
E. Hsu, Pulse Diagnosis in Early Chinese Medicine. New York, American: Cambridge University Press, 2010.
R. Amber and B. Brooke, Pulse Diagnosis: Detailed Interpretations For Eastern & Western Holistic Treatments. Santa Fe, New Mexico: Aurora Press, 1993.
Y. Chen, L. Zhang, D. Zhang, and D. Zhang, “Wrist pulse signal diagnosis using modified Gaussian Models and Fuzzy C-Means classification,” Medical Engineering & Physics, vol. 31, pp. 1283–1289, Dec 2009.
Y. Chen, L. Zhang, D. Zhang, and D. Zhang, “Computerized wrist pulse signal diagnosis using modified auto-regressive models,” Journal of Medical Systems, vol. 35, pp. 321–328, Jun 2011.
L. Liu, W. Zuo, D. Zhang, N. Li, and H. Zhang, “Combination of heterogeneous features for wrist pulse blood flow signal diagnosis via multiple kernel learning,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, pp. 599–607, Jul 2012.
L. Liu, W. Zuo, D. Zhang, N. Li, and H. Zhang, “Classification of wrist pulse blood flow signal using time warp edit distance,” Medical Biometrics, vol. 6165, pp. 137–144, 2010.
D. Y. Zhang, W. M. Zuo, D. Zhang, H. Z. Zhang, and N. M. Li, “Wrist blood flow signal-based computerized pulse diagnosis using spatial and spectrum features,” Journal of Biomedical Science and Engineering, vol. 3, pp. 361–366, 2010.
Q. L. Guo, K. Q. Wang, D. Y. Zhang, and N. M. Li, “A wavelet packet based pulse waveform analysis for cholecystitis and nephrotic syndrome diagnosis,” in IEEE International Conference on Wavelet Analysis and Pattern Recognition, Hong Kong, China, 2008, pp. 513–517.
S. Charbonnier, S. Galichet, G. Mauris, and J. P. Siche, “Statistical and fuzzy models of ambulatory systolic blood pressure for hypertension diagnosis,” IEEE Transactions on Instrumentation and Measurement, vol. 49, pp. 998–1003, 2000.
H.-T. Wu, C.-H. Lee, C.-K. Sun, J.-T. Hsu, R.-M. Huang, and C.-J. Tang, “Arterial Waveforms Measured at the Wrist as Indicators of Diabetic Endothelial Dysfunction in the Elderly,” IEEE Transactions on Instrumentation and Measurement, vol. 61, pp. 162–169, 2012.
H. Sorvoja, V. M. Kokko, R. Myllyla, and J. Miettinen, “Use of EMFi as a blood pressure pulse transducer,” IEEE Transactions on Instrumentation and Measurement, vol. 54, pp. 2505–2512, 2005.
E. Kaniusas, H. Pfutzner, L. Mehnen, J. Kosel, C. Tellez-Blanco, G. Varoneckas, et al., “Method for continuous nondisturbing monitoring of blood pressure by magnetoelastic skin curvature sensor and ECG,” IEEE Sensors Journal, vol. 6, pp. 819–828, Jun 2006.
C. Lianyi, H. Atsumi, M. Yagihashi, F. Mizuno, H. Narita, and H. Fujimoto, “A preliminary research on analysis of pulse diagnosis,” in IEEE International Conference on Complex Medical Engineering, Beijing,China, 2007, pp. 1807–1812.
H.-T. Wu, C.-H. Lee, and A.-B. Liu, “Assessment of endothelial function using arterial pressure signals,” Journal of Signal Processing Systems, vol. 64, pp. 223–232, 2011.
P. Renevey, R. Vetter, J. Krauss, P. Celka, and Y. Depeursinge, “Wrist-located pulse detection using IR signals, activity and nonlinear artifact cancellation,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001, pp. 3030–3033 vol.3.
P. Wang, W. Zuo, H. Zhang, and D. Zhang, “Design and implementation of a multi-channel pulse signal acquisition system,” in IEEE International Conference on Biomedical Engineering and Informatics, ChongQing, China, 2012, pp. 1063–1067.
C.-S. Hu, Y.-F. Chung, C.-C. Yeh, and C.-H. Luo, “Temporal and Spatial Properties of Arterial Pulsation Measurement Using Pressure Sensor Array,” Evidence-Based Complementary and Alternative Medicine, vol. 2012, pp. 1–9, 2012.
A. Zhang and H. Wang, “Real-time detection system For photoelectric pulse signals,” in IEEE International Conference on Business Management and Electronic Information Guangzhou,China, 2011, pp. 498–501.
N. Selvaraj, K. H. Shelley, D. G. Silverman, N. Stachenfeld, N. Galante, J. P. Florian, et al., “A Novel Approach Using Time Frequency Analysis of Pulse-Oximeter Data to Detect Progressive Hypovolemia in Spontaneously Breathing Healthy Subjects,” IEEE Transactions on Biomedical Engineering, vol. 58, pp. 2272–2279, 2011.
Q. Guo, K. Wang, D. Zhang, and N. Li, “A wavelet packet based pulse waveform analysis for cholecystitis and nephrotic syndrome diagnosis,” in IEEE International Conference on Wavelet Analysis and Pattern Recognition, Hong Kong, China, 2008, pp. 513–517.
I. Wakabayashi and H. Masuda, “Association of pulse pressure with fibrinolysis in patients with type 2 diabetes,” Thrombosis Research, vol. 121, pp. 95–102, 2007.
N. Arunkumar and K. M. M. Sirajudeen, “Approximate entropy based ayurvedic pulse diagnosis for diabetics – a case study,” in IEEE International Conference on Trendz in Information Sciences and Computing, Chennai,India, 2011, pp. 133–135.
Q. Y. Lee, G. S. H. Chan, S. J. Redmond, P. M. Middleton, E. Steel, P. Malouf, et al., “Classification of low systemic vascular resistance using photoplethysmogram and routine cardiovascular measurements,” in Annual International Conference of IEEE Engineering in Medicine and Biology Society, Buenos Aires, Argentina, 2010, pp. 1930–1933.
R. Murata, H. Kanai, N. Chubachi, and Y. Koiwa, “Measurement of local pulse wave velocity on aorta for noninvasive diagnosis of arteriosclerosis,” in IEEE Annual International Conference of the Engineering in Medicine and Biology Society, Baltimore, MD, 1994, pp. 83–84 vol.1.
A. C. Guyton and J. E. Hall, Textbook of medical physiology. Philadelphia, Pennsylvania: Elsevier, 2006.
ICNIRP, “ICNIRP Statement on Far Infrared Radiation Exposure,” Health Physics, vol. 91, pp. 630–645, 2006.
W. Schèaberle, Ultrasonography in Vascular Diagnosis: A Therapy-oriented Textbook and Atlas. Germany: Springer, 2005.
G. E. Mase, Schaum’s outline of theory and problems of continuum mechanics: McGraw-Hill New York, 1970.
M. Kutz, Biomedical Engineering and Design Handbook: Biomedical Engineering Fundamentals. United States: McGraw-Hill Professional, 2009.
J. R. Womersley, “Method for the calculation of velocity, rate of flow and viscous drag in arteries when the pressure gradient is known,” The Journal of physiology, vol. 127, pp. 553–563, 1955.
L. V. Wang and H.-i. Wu, Biomedical optics: principles and imaging. New Jersey: Wiley & Sons, 2012.
S. M. Finkelstein, W. Feske, J. Mock, P. Carlyle, T. Rector, S. Kubo, et al., “Vascular compliance in hypertension,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, New Orleans, LA, USA, 1988, pp. 241–242 vol.1.
S. M. Finkelstein, G. E. McVeigh, D. E. Burns, P. F. Carlyle, and J. N. Cohn, “Arterial Vascular Compliance In Heart Failure,” in 12th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Michigan,American, 1990, pp. 548–549.
J. Melo and J. I. Peters, “Low systemic vascular resistance: differential diagnosis and outcome,” Critical Care, vol. 3, pp. pp.71–77, 1999.
D. A. Fedosov, W. Pan, B. Caswell, G. Gompper, and G. E. Karniadakis, “Predicting human blood viscosity in silico,” Proceedings of the National Academy of Sciences, vol. 108, pp. 11772–11777, 2011.
W. A. N. Dorland, Illustrated medical dictionary: WB Saunders Company, 2011.
L. Xu, D. Zhang, and K. Wang, “Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms,” IEEE Transactions on Biomedical Engineering, vol. 52, pp. 1973–1975, Nov 2005.
L. Xu, M. Q. H. Meng, R. Liu, and K. Wang, “Robust peak detection of pulse waveform using height ratio,” in International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada, 2008, pp. 3856–3859.
D. Zhang, W. Zuo, D. Zhang, H. Zhang, and N. Li, “Wrist blood flow signal-based computerized pulse diagnosis using spatial and spectrum features,” Journal of Biomedical Science and Engineering, vol. 3, pp. 361–366, 2010.
C. Xia, Y. Li, J. Yan, Y. Wang, H. Yan, R. Guo, et al., “Wrist Pulse Waveform Feature Extraction and Dimension Reduction with Feature Variability Analysis,” in International Conference on Bioinformatics and Biomedical Engineering, Shanghai, China, 2008, pp. 2048–2051.
L. Xu, M. Q. H. Meng, K. Wang, W. Lu, and N. Li, “Pulse images recognition using fuzzy neural network,” Expert systems with applications, vol. 36, pp. 3805–3811, 2009.
Y. Wang, X. Wu, B. Liu, Y. Yi, and W. Wang, “Definition and application of indices in Doppler ultrasound sonogram,” Shanghai Journal of Biomedical Engineering, vol. 18, pp. 26–29, Aug 1997.
M. Costa, A. L. Goldberger, and C. K. Peng, “Multiscale entropy analysis of biological signals,” Physical Review E, vol. 71, pp. 1–18, Feb 2005.
M. Costa, A. L. Goldberger, and C. K. Peng, “Multiscale entropy analysis of complex physiologic time series,” Physical Review Letters, vol. 89, pp. 1–4, Aug 5 2002.
L. Liu, N. Li, W. Zuo, D. Zhang, and H. Zhang, “Multiscale sample entropy analysis of wrist pulse blood flow signal for disease diagnosis,” in Sino-foreign-interchange Workshop on Intelligence Science and Intelligent Data Engineering, NanJing China, 2012, pp. pp.475–482.
L. Liu, W. Zuo, D. Zhang, N. Li, and H. Zhang, “Classification of Wrist Pulse Blood Flow Signal Using Time Warp Edit Distance,” Medical Biometrics. Springer Berlin Heidelberg, pp. pp. 137–144, 2010.
M. Szafranski, Y. Grandvalet, and A. Rakotomamonjy, “Composite kernel learning,” Machine learning, vol. 79, pp. 73–103, 2010.
A. G. Lalkhen and A. McCluskey, “Clinical tests: sensitivity and specificity,” Continuing Education in Anaesthesia, Critical Care & Pain, vol. 8, pp. 221–223, 2008.
Q. McNemar, “Note on the sampling error of the difference between correlated proportions or percentages,” Psychometrika, vol. 12, pp. 153–157, June 1947.
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Zhang, D., Zuo, W., Wang, P. (2018). Comparison of Three Different Types of Wrist Pulse Signals. In: Computational Pulse Signal Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-10-4044-3_15
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DOI: https://doi.org/10.1007/978-981-10-4044-3_15
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