The Arterial Pressure Waveform (APW) can provide essential information about arterial wall integrity and arterial stiffness. Most of APW analysis frameworks individually process each hemodynamic parameter and do not evaluate inter-dependencies in the overall pulse morphology. The key contribution of this work is the use of machine learning algorithms to deal with vectorized features extracted from APW. With this purpose, we follow a five-step evaluation methodology: (1) a custom-designed, non-invasive, electromechanical device was used in the data collection from 50 subjects; (2) the acquired position and amplitude of onset, Systolic Peak (SP), Point of Inflection (Pi) and Dicrotic Wave (DW) were used for the computation of some morphological attributes; (3) pre-processing work on the datasets was performed in order to reduce the number of input features and increase the model accuracy by selecting the most relevant ones; (4) classification of the dataset was carried out using four different machine learning algorithms: Random Forest, BayesNet (probabilistic), J48 (decision tree) and RIPPER (rule-based induction); and (5) we evaluate the trained models, using the majority-voting system, comparatively to the respective calculated Augmentation Index (AIx). Classification algorithms have been proved to be efficient, in particular Random Forest has shown good accuracy (96.95%) and high area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve (0.961). Finally, during validation tests, a correlation between high risk labels, retrieved from the multi-parametric approach, and positive AIx values was verified. This approach gives allowance for designing new hemodynamic morphology vectors and techniques for multiple APW analysis, thus improving the arterial pulse understanding, especially when compared to traditional single-parameter analysis, where the failure in one parameter measurement component, such as Pi, can jeopardize the whole evaluation.
References
[1]
Cavalcante, J.L.; Lima, J.A.; Redheuil, A.; Al-Mallah, M.H. Aortic stiffness: Current understanding and future directions. J. Am. Coll. Cardiol.？2011, 57, 1511–1522, doi:10.1016/j.jacc.2010.12.017. 21453829
Safar, M.E.; Levy, B.I.; Boudier, H.S. Current perspectives on arterial stiffness and pulse pressure in hypertension and cardiovascular diseases. Circulation？2003, 107, 2864–2869, doi:10.1161/01.CIR.0000069826.36125.B4. 12796414
[4]
Blacher, J.; Safar, M.E. Large artery stiffness, hypertension and cardiovascular risk in older patients. Nat. Clin. Pract. Cardiovasc. Med.？2005, 2, 450–455, doi:10.1038/ncpcardio0307. 16265585
[5]
Safar, M. Arteries in Clinical Hypertension; Lippincott-Raven: Philadelphia, PA, USA, 1996; pp. 21–30.
[6]
Avolio, A.P.; Butlin, M.; Walsh, A. Arterial blood pressure measurement and pulse wave analysis—Their role in enhancing cardiovascular assessment. Physiol. Meas.？2010, 31, R1–R47, doi:10.1088/0967-3334/31/1/R01. 19940350
[7]
Eckerle, J.S. Encyclopedia of Medical Devices and Instrumentation; John Wiley & Sons Inc: New York, NY, USA, 1988; Volume 6. Chapter Arterial Tonometry, pp. 402–408.
[8]
Lee, J.; Nam, K.C. Tonometric Vascular Function Assessment. In Biomedical Engineering; InTech: Vukovar, Croatia, 2009; pp. 549–566.
[9]
Pereira, T.; Oliveira, T.; Cabeleira, M.; Matos, P.; Pereira, H.C.; Almeida, V.; Borges, E.; Santos, H.; Pereira, T.; Cardoso, J.; et al. Signal Analysis in a New Optical Pulse Waveform Profiler for Cardiovascular Applications. Proceedings of the Signal and Image Processing and Applications (SIPA 2011), Crete, Greece, 22–24 June 2011; Andreadis, M.Z., Ed.; ACTA Press: Crete, Greece, 2011; pp. 19–25.
[10]
Clemente, F.; Arpaia, P.; Cimmino, P. A piezo-film-based measurement system for global haemodynamic assessment. Physiol. Meas.？2010, 31, 697–714, doi:10.1088/0967-3334/31/5/007. 20395651
Almeida, V.G.; Pereira, H.C.; Pereira, T.; Figueiras, E.; Borges, E.; Cardoso, J.M.R.; Correia, C. Piezoelectric probe for pressure waveform estimation in flexible tubes and its applications to the cardiovascular system. Sens. Actuators A.？2011, 169, 217–226, doi:10.1016/j.sna.2011.04.048.
[13]
Scalzo, F.; Asgari, S.; Kim, S.; Bergsneider, M.; Hu, X. Robust peak recognition in intracranial pressure signals. Biomed. Eng. Online？2010, 9, e61, doi:10.1186/1475-925X-9-61.
[14]
Hu, X.; Xu, P.; Scalzo, F.; Vespa, P.; Bergsneider, M. Morphological clustering and analysis of continuous intracranial pressure. IEEE Trans. Biomed. Eng.？2009, 56, 696–705, doi:10.1109/TBME.2008.2008636. 19272879
[15]
Scalzo, F.; Asgari, S.; Kim, S.; Bergsneider, M.; Hu, X. Bayesian tracking of intracranial pressure signal morphology. Artif. Intell. Med.？2012, 54, 115–123, doi:10.1016/j.artmed.2011.08.007. 21968205
[16]
Nichols, W.W.; Singh, B.M. Augmentation index as a measure of peripheral vascular disease state. Curr. Opin. Cardiol.？2002, 17, 543–551, doi:10.1097/00001573-200209000-00016. 12357133
[17]
Jatoi, N.A.; Mahmud, A.; Bennett, K.; Feely, J. Assessment of arterial stiffness in hypertension: Comparison of oscillometric (Arteriograph), piezoelectronic (Complior) and tonometric (SphygmoCor) techniques. J. Hypertens？2009, 27, 2186–2191, doi:10.1097/HJH.0b013e32833057e8. 19834344
[18]
Complior Website. Available online: http://www.complior.com/products (accessed on 31 December 2012.
[19]
SphygmoCor Website. Available online: http://www.atcormedical.com/sphygmocor.html (accessed on 31 December 2012).
[20]
Tsipouras, M.G.; Exarchos, T.P.; Fotiadis, D.I.; Kotsia, A.; Vakalis, K.; Naka, K.K.; Michalis, L.K. Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling. IEEE Trans. Inf. Technol. Biomed.？2008, 12, 447–458, doi:10.1109/TITB.2007.907985. 18632325
[21]
Jovic, A.; Bogunovic, N. Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features. Artif. Intell. Med.？2011, 51, 175–186, doi:10.1016/j.artmed.2010.09.005. 20980134
Asl, B.M.; Setarehdan, S.K.; Mohebbi, M. Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artif. Intell. Med.？2008, 44, 51–64, doi:10.1016/j.artmed.2008.04.007. 18585905
[24]
Ting, S.L.; Shym, C.C.; Kwork, S.K.; Tsang, A.H.C.; Lee, W.B. Data mining in biomedicine: Current applications and further directions for research. J. Softw. Eng. Appl.？2009, 2, 150–159, doi:10.4236/jsea.2009.23022.
Ruta, D.; Gabrys, B. An overview of classifier fusion methods. Comput. Inf. Syst.？2000, 7, 1–10.
[27]
Almeida, V.G.; Santos, P.; Figueiras, E.; Borges, E.; Pereira, T.; Cardoso, J.; Correia, C.; Pereira, H.C. Hemodynamic Features Extraction from a New Arterial Pressure Waveform Probe. In Proceedings of the 4th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2011), Rome, Italy, 26–29 January 2011.
[28]
Murgo, J.; Westerhof, N.; Giolma, J.P.; Altobelli, S. Aortic input impedance in normal man: Relationship to pressure wave forms, Circulation. Circulation？1980, 62, 105–116, doi:10.1161/01.CIR.62.1.105. 7379273
[29]
Weka 3: Data Mining Software in Java. Available online: http://www.cs.waikato.ac.nz/ml/weka/ (accessed on 30 June 2012).
[30]
Witten, I.H.; Frank, E.; Hall, M.A. DataMining: Practical Machine Learning Tools and Techniques, 2nd ed. ed.; Morgan Kaufmann: San Francisco, CA, USA, 2005.
[31]
Kumari, M.; Godara, S. Comparative study of data mining classification methods in cardiovascular disease prediction. Int. J. Comput. Sci. Telecommun.？2011, 2, 303–308.
[32]
Dranca, L.; Goni, A.; Illarramendi, A. Real-time detection of transient cardiac ischemic episodes from ECG signals. Physiol. Meas.？2009, 30, 983–998, doi:10.1088/0967-3334/30/9/009. 19696464
[33]
Han, J.; Kamber, M. Data Mining Concepts and Techniques; Elsevier: San Francisco, CA, USA, 2006; pp. 291–296.
[34]
Breiman, L. Random forests. Mach. Learn.？2001, 45, 5–32, doi:10.1023/A:1010933404324.
[35]
Gayatri, N.S.N.; ; Reddy, A.V.; Chitra, R. Performance Analysis of Datamining Algorithms for Software Quality Prediction. In Proceedings of the International Conference on Advances in Recent Technologies in Communication and Computing, Kottayam, India, 27–28 October 2009; Curran Associates, Inc.: Kottayam, India, 2009; pp. 393–395.
[36]
Nguyen, H.A.; Choi, D. Application of Data Mining to Network Intrusion Detection: Classifier Selection Model. In Proceedings of the 11th Asia-Pacific network Operations and Management Symposium (APNOMS), Beijing, China, 22–24 October 2008; Springer-Verlag: Berlin/Heidelberg, Germany, 2008; pp. 399–408.
[37]
Perk, J.; de Backer, G.; Gohlke, H.; Graham, I.; Reiner, Z.; Verschuren, M.; Albus, C.; Benlian, P.; Boysen, G.; Cifkova, R.; et al. European Guidelines on cardiovascular disease prevention in clinical practice (version 2012). The Fifth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of nine societies and by invited experts). Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur. Heart J.？2012, 33, 1635–1701, doi:10.1093/eurheartj/ehs092. 22555213
[38]
Yoo, I.; Alafaireet, P.; Marinov, M.; Pena-Hernandez, K.; Gopidi, R.; Chang, J.F.; Hua, L. Data mining in healthcare and biomedicine: A survey of the literature. J. Med. Syst.？2011, 36, 2431–2448. 21537851
[39]
McEniery, C.M.; Yasmin; Hall, I.R.; Qasem, A.; Wilkinson, I.B.; Cockcroft, J.R. Normal vascular aging: Differential effects on wave reflection and aortic pulse wave velocity: The Anglo-Cardiff Collaborative Trial (ACCT). J.Am. Coll. Cardiol.？2005, 46, 1753–1760, doi:10.1016/j.jacc.2005.07.037. 16256881
