J. Pers. Med. 2013, 3(2), 82-101; doi:10.3390/jpm3020082
Article

Machine Learning Techniques for Arterial Pressure Waveform Analysis

1,* email, 1, 1, 1, 1,2, 1, 3 and 1
Received: 1 March 2013; in revised form: 18 April 2013 / Accepted: 25 April 2013 / Published: 2 May 2013
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: 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.
Keywords: pulse wave analysis; arterial pressure waveform; machine learning; arterial stiffness; augmentation index
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MDPI and ACS Style

Almeida, V.G.; Vieira, J.; Santos, P.; Pereira, T.; Pereira, H.C.; Correia, C.; Pego, M.; Cardoso, J. Machine Learning Techniques for Arterial Pressure Waveform Analysis. J. Pers. Med. 2013, 3, 82-101.

AMA Style

Almeida VG, Vieira J, Santos P, Pereira T, Pereira HC, Correia C, Pego M, Cardoso J. Machine Learning Techniques for Arterial Pressure Waveform Analysis. Journal of Personalized Medicine. 2013; 3(2):82-101.

Chicago/Turabian Style

Almeida, Vânia G.; Vieira, João; Santos, Pedro; Pereira, Tânia; Pereira, H. C.; Correia, Carlos; Pego, Mariano; Cardoso, João. 2013. "Machine Learning Techniques for Arterial Pressure Waveform Analysis." J. Pers. Med. 3, no. 2: 82-101.

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