Blood pressure estimation from appropriate and inappropriate PPG signals using A whole-based method

Abstract Background and objective Blood pressure (BP) is one of the four vital signals that provides valuable medical information about the cardiovascular activity. In recent years, extensive studies have been conducted on non-invasive and cuff-less BP estimation using photoplethysmography (PPG) signals. PPG is a non-invasive optical method for measuring blood volume changes per pulse. In other words, the PPG waveform represents the mechanical activity of the heart. Methods In this paper, a new method for estimating the Mean Arterial Pressure (MAP), Diastolic Blood Pressure (DBP) and Systolic Blood Pressure (SBP) is proposed using only the PPG signal regardless of its shape (appropriate or inappropriate). Our proposed algorithm called whole-based, uses raw values of the PPG signal at a given time interval for estimating the BP. In other algorithms called parameter-based, use features which are extracted from PPG signals in time or frequency domain. These features related to precise spotting in the form of the PPG signal. In fact, compared to parameter-based methods, our algorithm is independent of the form of the PPG signal. Results Using the proposed algorithm, our results are completely met by the Association for the Advancement of Medical Instrumentation (AAMI) standard for both MAP and DBP estimations. The results are also very close to the standard boundary with an average error close to zero for SBP estimation. Also, according to the British Hypertension Society (BHS) standard, the proposed algorithm for DBP estimation got grade A, whereas it got grade B for estimation of MAP and got approximately grade C for SBP estimation. Conclusion The results demonstrate the applicability of the proposed algorithm in estimating BP noninvasively, cuff-less, calibration-free, and only by using the appropriate or inappropriate PPG signal.

[1]  Mehrdad Nourani,et al.  An adaptive deep learning approach for PPG-based identification , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[2]  Martin Wolf,et al.  An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals , 2012, Algorithms.

[3]  Paul A. Iaizzo.  Handbook of Cardiac Anatomy, Physiology, and Devices , 2005 .

[4]  Mehmet Rasit Yuce,et al.  A survey on signals and systems in ambulatory blood pressure monitoring using pulse transit time , 2015, Physiological measurement.

[5]  Yue Zhang,et al.  A Novel Frequency Domain Method for Estimating Blood Pressure from Photoplethysmogram , 2017, ICSPS 2017.

[6]  Hoda Mohammadzade,et al.  Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

[7]  Guoxing Wang,et al.  A feature exploration methodology for learning based cuffless blood pressure measurement using photoplethysmography , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[8]  He Ma,et al.  A Novel Indicator for Cuff-Less Blood Pressure Estimation Based on Photoplethysmography , 2016, HIS.

[9]  Wen Jun Jiang,et al.  Data-driven estimation of blood pressure using photoplethysmographic signals , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[10]  Domenico Grimaldi,et al.  A Neural Network-based method for continuous blood pressure estimation from a PPG signal , 2013, 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[11]  Evgeny Bogdanov,et al.  Photoplethysmography-Based Device Designing for Cardiovascular System Diagnostics , 2015 .

[12]  G Parati,et al.  Blood pressure measuring devices: recommendations of the European Society of Hypertension , 2001, BMJ : British Medical Journal.

[13]  Richard Kronland-Martinet,et al.  A real-time algorithm for signal analysis with the help of the wavelet transform , 1989 .

[14]  Egidijus Kazanavicius,et al.  Blood Pressure Estimation Accoording to Photoplethysmographic Signal Steepness , 2015, Inf. Technol. Control..

[15]  John Allen.  Photoplethysmography and its application in clinical physiological measurement , 2007, Physiological measurement.

[16]  Arpan Pal,et al.  Novel Statistical Post Processing to Improve Blood Pressure Estimation from Smartphone Photoplethysmogram , 2017, HumanSys@SenSys.

[17]  Mingshan Sun,et al.  Optical blood pressure estimation with photoplethysmography and FFT-based neural networks. , 2016, Biomedical optics express.

[18]  J. Ruiz-Rodríguez,et al.  Innovative continuous non-invasive cuffless blood pressure monitoring based on photoplethysmography technology , 2013, Intensive Care Medicine.

[19]  Aniruddha Sinha,et al.  Estimating blood pressure using Windkessel model on photoplethysmogram , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Enric Monte-Moreno,et al.  Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques , 2011, Artif. Intell. Medicine.

[21]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[22]  Mahdi Shabany,et al.  Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring , 2017, IEEE Transactions on Biomedical Engineering.

[23]  Carmen C. Y. Poon,et al.  Pulse Arrival Time Based Cuff-Less and 24-H Wearable Blood Pressure Monitoring and its Diagnostic Value in Hypertension , 2016, Journal of Medical Systems.

[24]  Chelli N. Devi,et al.  Non-invasive Estimation of Oxygen Saturation Level in Blood , 2017 .

[25]  Janis Spigulis,et al.  Potential of advanced photoplethysmography sensing for noninvasive vascular diagnostics and early screening , 2002, SPIE BiOS.

[26]  Majid Sarrafzadeh,et al.  Building Continuous Arterial Blood Pressure Prediction Models Using Recurrent Networks , 2016, 2016 IEEE International Conference on Smart Computing (SMARTCOMP).

[27]  Peter Scarborough,et al.  Cardiovascular disease in Europe: epidemiological update. , 2014, European heart journal.

[28]  L A Geddes,et al.  Pulse transit time as an indicator of arterial blood pressure. , 1981, Psychophysiology.

[29]  J. Kostamovaara,et al.  Photoplethysmography and its detailed pulse waveform analysis for arterial stiffness , 2011 .

[30]  Norbert Noury,et al.  A review of methods for non-invasive and continuous blood pressure monitoring: Pulse transit time method is promising? , 2014 .

[31]  Homer Nazeran,et al.  Wireless photoplethysmographic device for heart rate variability signal acquisition and analysis , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  Lai-Man Po,et al.  Cuffless Blood Pressure Estimation Based on Photoplethysmography Signal and Its Second Derivative , 2017 .

[33]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[34]  Mohammad Hassan Moradi,et al.  Cuffless Blood Pressure Estimation during Exercise Stress Test , 2012 .

[35]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[36]  M. Elgendi.  On the Analysis of Fingertip Photoplethysmogram Signals , 2012, Current cardiology reviews.

[37]  P. Chowienczyk,et al.  Noninvasive Assessment of the Digital Volume Pulse: Comparison With the Peripheral Pressure Pulse , 2000, Hypertension.

[38]  Maram Maheedhar,et al.  Cuff-less PPG based continuous blood pressure monitoring - A smartphone based approach , 2016, EMBC.

[39]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[40]  Yue Zhang,et al.  A SVM Method for Continuous Blood Pressure Estimation from a PPG Signal , 2017, ICMLC.

[41]  Arata Suzuki,et al.  Feature Selection Method for Estimating Systolic Blood Pressure Using the Taguchi Method , 2014, IEEE Transactions on Industrial Informatics.

[42]  Dorairaj Prabhakaran,et al.  The burden of blood pressure-related disease: a neglected priority for global health. , 2007, Hypertension.

[43]  S. Lemon,et al.  Classification and regression tree analysis in public health: Methodological review and comparison with logistic regression , 2003, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[44]  Zhilin Zhang,et al.  TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise , 2014, IEEE Transactions on Biomedical Engineering.

[45]  Toshiyo Tamura,et al.  Wearable Photoplethysmographic Sensors—Past and Present , 2014 .

[46]  Y.T. Zhang,et al.  Continuous and noninvasive estimation of arterial blood pressure using a photoplethysmographic approach , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[47]  Harris Drucker,et al.  Improving Regressors using Boosting Techniques , 1997, ICML.

[48]  Yuan-Ting Zhang,et al.  Continuous Blood Pressure Measurement From Invasive to Unobtrusive: Celebration of 200th Birth Anniversary of Carl Ludwig , 2016, IEEE J. Biomed. Health Informatics.

[49]  Ming Liu,et al.  Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography , 2016, Science China Information Sciences.

[50]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .