Name
Abstract | ||
|---|---|---|
In this paper, we present a novel feature-based neural network (NN) approach for estimation of blood pressure (BP) from wrist oscillometric measurements. Unlike previous methods that use the raw oscillometric waveform envelope (OMWE) as input to the NN, in this paper, we propose to use features extracted from the envelope. The OMWE is mathematically modeled as a sum of two Gaussian functions. The optimum parameters of this model are found by minimizing the least squares error between the model and the OMWE using the Levenberg-Marquardt algorithm and are used as features. Two separate feed-forward NNs (FFNNs) are then designed to estimate the systolic and diastolic BPs using these features. The FFNNs are trained using the resilient backpropagation learning algorithm and tested on a data set of BP measurements recorded from 85 subjects. The performance is then compared with that of the conventional maximum amplitude algorithm, adaptive neuro-fuzzy inference system, and already published NN-based methods. It is found that the proposed approach achieves lower values of mean absolute error and standard deviation of error in the estimation of BP. In addition, the proposed approach has the following advantages: lower complexity with respect to the design parameters, smaller training data set, and lower computational load. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TIM.2011.2123210 | Instrumentation and Measurement, IEEE Transactions |
Keywords | Field | DocType |
backpropagation,blood pressure measurement,feature extraction,fuzzy logic,gradient methods,least squares approximations,medical signal processing,neural nets,Gaussian function sum,Levenberg-Marquardt algorithm,OMWE,adaptive neurofuzzy inference system,diastolic blood pressure estimation,envelope extracted features,feature based neural network approach,feed forward neural nets,least squares error minimisation,maximum amplitude algorithm,oscillometric blood pressure estimation,raw oscillometric waveform envelope,resilient backpropagation learning algorithm,systolic blood pressure estimation,wrist oscillometric measurements,Blood pressure (BP),estimation,feature extraction,neural network (NN),oscillometric measurement | Pattern recognition,Computer science,Waveform,Fuzzy logic,Feature extraction,Gaussian,Artificial intelligence,Artificial neural network,Backpropagation,Rprop,Standard deviation | Journal |
Volume | Issue | ISSN |
60 | 8 | 0018-9456 |
Citations | PageRank | References |
7 | 0.83 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamad Forouzanfar | 1 | 67 | 9.45 |
| Hilmi R. Dajani | 2 | 105 | 16.16 |
| Voicu Z. Groza | 3 | 51 | 9.03 |
| Miodrag Bolic | 4 | 503 | 58.17 |
| Sreeraman Rajan | 5 | 219 | 34.94 |