A novel approach for non-invasive measurement of mean arterial pressure using pulse transit time

Recent days many researches are carried out related to the heart of the human. As a result many remedies are found for the various heart disorders. Electrocardiography (ECG) is the exertion of the electrical response from heart by placing the electrodes over the chest. The Einthoven triangle is an imaginary formation of the three leads in the triangle used in electrocardiography; by that technique of placement of electrode we can analysis the electrocardiogram of the heart. The waveform is also known as PQR waveform which contains the information of arterial repolarization, arterial polarization, ventricular repolarization and ventricular polarization. In general the BP monitor is carried out using oscillometry. In the existing system separate techniques and devices are used to measure electrocardiography and BP. The proposed system is to integrate both electrocardiography and blood pressure measurement by means adopting transit pulse time. It is defined as the systematic time lay off betwixt oscillometric pulses and peaks of ECG especially R peaks of it. The major advantage of the system is to exhibit non-zero crossing and gives more accurate result. The mean arterial pressure estimation is done by using MatLab which is the unique estimation method for measuring pulse transit time interval. Thus the non invasive system designed which reduce the cuff deviations and errors in electrocardiography. It can be applied in detecting the arterial disorders at most efficient manner.

[1]  G. V. van Montfrans,et al.  Oscillometric blood pressure measurement: progress and problems , 2001, Blood pressure monitoring.

[2]  Wendy Van Moer,et al.  Using Alternating Kalman Filtering to Analyze Oscillometric Blood Pressure Waveforms , 2013, IEEE Transactions on Instrumentation and Measurement.

[3]  Joon-Hyuk Chang,et al.  Oscillometric Blood Pressure Estimation Based on Maximum Amplitude Algorithm Employing Gaussian Mixture Regression , 2013, IEEE Transactions on Instrumentation and Measurement.

[4]  P. G. Kuppusamy,et al.  Evaluation and analysis of data driven in expectation maximization segmentation through various initialization techniques in medical images , 2018, Multimedia Tools and Applications.

[5]  J. Talts,et al.  Mathematical modelling of non-invasive oscillometric finger mean blood pressure measurement by maximum oscillation criterion , 1999, Medical & Biological Engineering & Computing.

[6]  G. Gunasekaran,et al.  Fuzzy Logic based Nam Speech Recognition for Tamil Syllables , 2016 .

[7]  Voicu Groza,et al.  Ratio-Independent Blood Pressure Estimation by Modeling the Oscillometric Waveform Envelope , 2014, IEEE Transactions on Instrumentation and Measurement.

[8]  Voicu Groza,et al.  Coefficient-Free Blood Pressure Estimation Based on Pulse Transit Time–Cuff Pressure Dependence , 2013, IEEE Transactions on Biomedical Engineering.

[9]  Voicu Groza,et al.  Electrocardiogram-Assisted Blood Pressure Estimation , 2012, IEEE Transactions on Biomedical Engineering.

[10]  K. Dhanalakshmi,et al.  An intelligent mining system for diagnosing medical images using combined texture‐histogram features , 2013, Int. J. Imaging Syst. Technol..

[11]  M. Nitzan,et al.  Automatic noninvasive measurement of arterial blood pressure , 2011, IEEE Instrumentation & Measurement Magazine.

[12]  Liqiang Zheng,et al.  Pulse Pressure and Mean Arterial Pressure in Relation to Ischemic Stroke Among Patients With Uncontrolled Hypertension in Rural Areas of China , 2008, Stroke.

[13]  YJ Chee,et al.  Mean arterial pressure estimation method using morphological changes in oscillometric waveform , 2009, 2009 36th Annual Computers in Cardiology Conference (CinC).

[14]  M. Bolic,et al.  Arterial blood pressure parameter estimation and tracking using particle filters , 2011, 2011 IEEE International Symposium on Medical Measurements and Applications.

[15]  José Antonio de la O. Serna Taylor–Fourier Analysis of Blood Pressure Oscillometric Waveforms , 2012, IEEE Transactions on Instrumentation and Measurement.

[16]  Dingchang Zheng,et al.  Estimation of mean arterial pressure from the oscillometric cuff pressure: comparison of different techniques , 2010, Medical & Biological Engineering & Computing.

[17]  A. Murray,et al.  Estimation of mean blood pressure from oscillometric and manual methods , 2008, 2008 Computers in Cardiology.

[18]  P. Senthil Kumar,et al.  GALS implementation of randomly prioritized buffer-less routing architecture for 3D NoC , 2018, Cluster Computing.

[19]  John G. Wood,et al.  MCDONALDʼS BLOOD FLOW IN ARTERIES: THEORETICAL, EXPERIMENTAL AND CLINICAL PRINCIPLES, 4TH EDITION , 1998 .

[20]  E. Dhiravidachelvi,et al.  A Novel Approach for Diagnosing Diabetic Retinopathy in Fundus Images , 2015, J. Comput. Sci..

[21]  Chin-Teng Lin,et al.  Reduction of interference in oscillometric arterial blood pressure measurement using fuzzy logic , 2003, IEEE Trans. Biomed. Eng..

[22]  Alan Murray,et al.  Automatic blood pressure measurement: the oscillometric waveform shape is a potential contributor to differences between oscillometric and auscultatory pressure measurements , 2008, Journal of hypertension.

[23]  Voicu Groza,et al.  Blood pressure estimation using oscillometric pulse morphology , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[24]  P. Senthil Kumar,et al.  Randomly prioritized buffer-less routing architecture for 3D Network on Chip , 2017, Comput. Electr. Eng..