Feature-Based Neural Network Approach for Oscillometric Blood Pressure Estimation

Forouzanfar, Mohamad; Dajani, Hilmi R.; Groza, Voicu; Bolić, Miodrag; Rajan, Sreeraman

doi:10.1109/tim.2011.2123210

Cited by 73 publications

(36 citation statements)

References 29 publications

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“…Normalization is performed to ensure that all descriptor components lie in the interval [−0.95, +0.95]. Weight initialization is done following the Nguyen-Widrow method [57,58] so that the active regions of the hidden neurons are distributed approximately evenly over the input space. Finally, we make use of iRprop [59] to optimize the network weights.…”

Section: Resultsmentioning

confidence: 99%

Vision-Based Corrosion Detection Assisted by a Micro-Aerial Vehicle in a Vessel Inspection Application

Ortiz

Bonnín-Pascual

Garcia-Fidalgo

et al. 2016

Sensors

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Vessel maintenance requires periodic visual inspection of the hull in order to detect typical defective situations of steel structures such as, among others, coating breakdown and corrosion. These inspections are typically performed by well-trained surveyors at great cost because of the need for providing access means (e.g., scaffolding and/or cherry pickers) that allow the inspector to be at arm’s reach from the structure under inspection. This paper describes a defect detection approach comprising a micro-aerial vehicle which is used to collect images from the surfaces under inspection, particularly focusing on remote areas where the surveyor has no visual access, and a coating breakdown/corrosion detector based on a three-layer feed-forward artificial neural network. As it is discussed in the paper, the success of the inspection process depends not only on the defect detection software but also on a number of assistance functions provided by the control architecture of the aerial platform, whose aim is to improve picture quality. Both aspects of the work are described along the different sections of the paper, as well as the classification performance attained.

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Section: Resultsmentioning

confidence: 99%

Vision-Based Corrosion Detection Assisted by a Micro-Aerial Vehicle in a Vessel Inspection Application

Ortiz

Bonnín-Pascual

Garcia-Fidalgo

et al. 2016

Sensors

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“…Neural networks have been extensively used since the nineties for data identification [13], sensor data processing [14] and feature extraction [15] specifically when the model linking raw data and desired features are unknown. Even though many different topologies have been employed [16], the conventional Multi Layer Perceptron (MLP) is one of the most common topologies.…”

Section: Mass Transfer Coefficient Estimation Via Annmentioning

confidence: 99%

Wavelet image decomposition for characterization of freeze-dried pharmaceutical product structures

Grassini

Angelini

Pisano

et al. 2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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This paper investigates the use of a wavelet image decomposition applied to electron microscope images in order to estimate the mass transfer coefficient of pharmaceutical cakes obtained by freeze-drying. The structure analysis of dried cakes obtained by means of a free-drying process, is a basic step for tuning the process conditions and for monitoring the quality of the dried product. The product structure and specifically its porosity affects the drying duration as it defines the resistance to the vapor flow during the ice sublimation. This parameter is becoming quite important as it is fundamental for modeling of the freeze-drying process and thus for an optimal design of the freeze-drying cycle. The direct measurement of this parameter is quite complex thus new simple approaches are being developed for its non-invasive estimation. This paper discusses the possibility of processing SEM images of the dried cake to analyze its morphology and to estimate the mass transfer coefficient. This approach has already been followed by processing the images via a 2D-FFT, here a faster solution based on the image wavelet decomposition followed by a non-linear processing based on an artificial neural network is described and the results are compared with the one obtained by the traditional direct mass transfer coefficient measurement.

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“…They concluded that true positive rates (TPRs) and false positive rates (FPRs) were improved during the prediction period. Forouzanfar et al (2011) presented a novel feature-based ANN for the estimate of BP from wrist oscillometric measurements. Unlike previous methods that use the raw oscillometric waveform envelope (OMWE) as an input to ANN, they used features extracted from the envelope.…”

Section: Introductionmentioning

confidence: 99%

Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Kaur

Arora

Jain

2015

Frontiers Inf Technol Electronic Eng

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Accurate blood pressure (BP) measurement is essential in epidemiological studies, screening programmes, and research studies as well as in clinical practice for the early detection and prevention of high BP-related risks such as coronary heart disease, stroke, and kidney failure. Posture of the participant plays a vital role in accurate measurement of BP. Guidelines on measurement of BP contain recommendations on the position of the back of the participants by advising that they should sit with supported back to avoid spuriously high readings. In this work, principal component analysis (PCA) is fused with forward stepwise regression (SWR), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and the least squares support vector machine (LS-SVM) model for the prediction of BP reactivity to an unsupported back in normotensive and hypertensive participants. PCA is used to remove multi-collinearity among anthropometric predictor variables and to select a subset of components, termed 'principal components' (PCs), from the original dataset. The selected PCs are fed into the proposed models for modeling and testing. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that a PCA-based LS-SVM (PCA-LS-SVM) model is a promising approach for the prediction of BP reactivity in comparison to others. This assessment demonstrates the importance and advantages posed by hybrid models for the prediction of variables in biomedical research studies.

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Feature-Based Neural Network Approach for Oscillometric Blood Pressure Estimation

Cited by 73 publications

References 29 publications

Vision-Based Corrosion Detection Assisted by a Micro-Aerial Vehicle in a Vessel Inspection Application

Vision-Based Corrosion Detection Assisted by a Micro-Aerial Vehicle in a Vessel Inspection Application

Wavelet image decomposition for characterization of freeze-dried pharmaceutical product structures

Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

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