A comparison of three types of pulse signals: Physical meaning and diagnosis performance

Pulse diagnosis has been extensively applied in China and Ayuredic for thousands of years. Recently more and more research interests have been given on computerized pulse diagnosis where sensor techniques are used to acquire the pulse signal and machine learning techniques are adopted to analyze the health condition based on the acquired pulse signals. By far, a number of sensors had been employed for pulse signal acquisition, which can be grouped into three categories, i.e., the pressure sensor, the photoelectric sensor, and the ultrasound sensor. To guide the sensor selection for computational pulse diagnosis, in this paper we analyze the physical meanings and sensitivities of signals sampled by these three types of sensors. The complementary information of different sensors is discussed from both cardiovascular fluid dynamics and comparative experiments by evaluating the disease classification performance. Signals acquired using different sensors are sensitive to different physiological and pathological factors. By combining signals from different sensor, improved diagnosis performance can be obtained.

[1]  M. Dauzat,et al.  Ultrasonography in vascular diagnosis , 2005 .

[2]  H. Masuda,et al.  Association of pulse pressure with fibrinolysis in patients with type 2 diabetes. , 2007, Thrombosis research.

[3]  Gerhard Gompper,et al.  Predicting human blood viscosity in silico , 2011, Proceedings of the National Academy of Sciences.

[4]  Lei Liu,et al.  Classification of Wrist Pulse Blood Flow Signal Using Time Warp Edit Distance , 2010, ICMB.

[5]  A. Guyton,et al.  Textbook of Medical Physiology , 1961 .

[6]  David Zhang,et al.  Multiscale Sample Entropy Analysis of Wrist Pulse Blood Flow Signal for Disease Diagnosis , 2012, IScIDE.

[7]  David Zhang,et al.  Combination of Heterogeneous Features for Wrist Pulse Blood Flow Signal Diagnosis via Multiple Kernel Learning , 2012, IEEE Transactions on Information Technology in Biomedicine.

[8]  Lian-yi Chen,et al.  A preliminary Research on Analysis of Pulse Diagnosis , 2007, 2007 IEEE/ICME International Conference on Complex Medical Engineering.

[9]  Stanley M. Finkelstein,et al.  Arterial Vascular Compliance In Heart Failure , 1990, [1990] Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Stanley M. Finkelstein,et al.  Vascular compliance in hypertension , 1988, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  L. Geddes,et al.  Measurements of Young's Modulus of Elasticity of the Canine Aorta with Ultrasound , 1979 .

[12]  Noriyoshi Chubachi,et al.  Measurement of local pulse wave velocity on aorta for noninvasive diagnosis of arteriosclerosis , 1994, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  G. Ziegelberger ICNIRP STATEMENT ON FAR INFRARED RADIATION EXPOSURE , 2006, Health physics.

[14]  J. Peters,et al.  Low systemic vascular resistance: differential diagnosis and outcome , 1999, Critical care.

[15]  Sylvie Galichet,et al.  Statistical and fuzzy models of ambulatory systolic blood pressure for hypertension diagnosis , 2000, IEEE Trans. Instrum. Meas..

[16]  David Zhang,et al.  Computerized Wrist Pulse Signal Diagnosis Using Modified Auto-Regressive Models , 2011, Journal of Medical Systems.

[17]  T. Meydan,et al.  Method for continuous nondisturbing monitoring of blood pressure by magnetoelastic skin curvature sensor and ECG , 2006, IEEE Sensors Journal.

[18]  S. Walsh,et al.  Pulse Diagnosis: A Clinical Guide , 2007 .

[19]  David Zhang,et al.  Design and implementation of a multi-channel pulse signal acquisition system , 2012, 2012 5th International Conference on BioMedical Engineering and Informatics.

[20]  W. Zuo,et al.  Learning with multiple Gaussian distance kernels for time series classification , 2011, 2011 3rd International Conference on Advanced Computer Control.

[21]  J. Womersley Method for the calculation of velocity, rate of flow and viscous drag in arteries when the pressure gradient is known , 1955, The Journal of physiology.

[22]  Hsien-Tsai Wu,et al.  Arterial Waveforms Measured at the Wrist as Indicators of Diabetic Endothelial Dysfunction in the Elderly , 2012, IEEE Transactions on Instrumentation and Measurement.

[23]  Elisabeth Hsu,et al.  Pulse Diagnosis in Early Chinese Medicine: The Telling Touch , 2010 .

[24]  N. Arunkumar,et al.  Approximate Entropy based ayurvedic pulse diagnosis for diabetics - a case study , 2011, 3rd International Conference on Trendz in Information Sciences & Computing (TISC2011).

[25]  Hannu Sorvoja,et al.  Use of EMFi as a blood pressure pulse transducer , 2005, IEEE Transactions on Instrumentation and Measurement.

[26]  Colin Deane Ultrasonography in Vascular Diagnosis. A Therapy-Oriented Textbook and Atlas , 2012 .

[27]  Myer Kutz,et al.  Biomedical engineering and design handbook , 2009 .

[28]  W. Zuo,et al.  Wrist blood flow signal-based computerized pulse diagnosis using spatial and spectrum features , 2010 .

[29]  T. Tamura,et al.  27th Annual Inter national Conference of the IEEE Engineering in Medicine and Biology Society , 2005 .

[30]  Kuan-Quan Wang,et al.  A wavelet packet based pulse waveform analysis for cholecystitis and nephrotic syndrome diagnosis , 2008, 2008 International Conference on Wavelet Analysis and Pattern Recognition.

[31]  David Zhang,et al.  Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms , 2005, IEEE Transactions on Biomedical Engineering.

[32]  Hsien-Tsai Wu,et al.  Assessment of Endothelial Function Using Arterial Pressure Signals , 2011, J. Signal Process. Syst..

[33]  Aihua Zhang,et al.  Real-time detection system For photoelectric pulse signals , 2011, 2011 International Conference on Business Management and Electronic Information.

[34]  Nigel H. Lovell,et al.  Classification of low systemic vascular resistance using photoplethysmogram and routine cardiovascular measurements , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[35]  David Zhang,et al.  Wrist pulse signal diagnosis using modified Gaussian models and Fuzzy C-Means classification. , 2009, Medical engineering & physics.