Lieve Lauwers

Linearizing Oscillometric Blood-Pressure Measurements: (Non)Sense?

This paper proposes a simplified method to compute the systolic and diastolic blood pressures from measured oscillometric blood-pressure waveforms. Therefore, the oscillometric waveform is analyzed in the frequency domain, which reveals that the measured blood-pressure signals are heavily disturbed by nonlinear contributions. The proposed approach will linearize the measured oscillometric waveform in order to obtain a more accurate and transparent estimation of the systolic and diastolic pressure based on a robust preprocessing technique. This new approach will be compared with the Korotkoff method and a commercially available noninvasive blood-pressure meter. This allows verification if the linearized approach contains as much information as the Korotkoff method in order to calculate a correct systolic and diastolic blood pressure.

A Nonlinear Block Structure Identification Procedure Using Frequency Response Function Measurements

Based on simple frequency response function (FRF) measurements, we give the user some guidance in the selection of an appropriate nonlinear block structure for the system to be modeled. The method consists in measuring the FRF using a Gaussian-like input signal and varying in a first experiment the root-mean-square (rms) value of this signal while maintaining the coloring of the power spectrum. Next, in a second experiment, the coloring of the power spectrum is varied while keeping the rms value constant. Based on the resulting behavior of the FRF, an appropriate nonlinear block structure can be selected to approximate the real system. The identification of the selected block-oriented model itself is not addressed in this paper. A theoretical analysis and two practical applications of this structure identification method are presented for some nonlinear block structures.

Estimating the parameters of a Rice distribution: A Bayesian approach

The problem of detecting a periodic signal buried in zero-mean Gaussian noise is present in various fields of engineering. It is well-known that the amplitude of the disturbed signal follows a Rice distribution which is characterized by two parameters. In this paper, an alternative Bayesian approach is proposed to tackle this two-parameter estimation problem. By incorporating prior knowledge into a mathematical framework, the drawbacks of the existing methods (i.e., the maximum likelihood approach and the method of moments) can be overcome. The performance of the proposed Bayesian estimatoris shown through simulations.

Functional Magnetic Resonance Imaging: An Improved Short Record Signal Model

The number of measurement problems for which it is either difficult or expensive to obtain long measurement records is rising. This short-record issue particularly pops up in biomedical measurements. In this paper, we study the problem of modeling fMRI signals used to map brain activity. In this paper, it is shown that, by using a postprocessing technique that is well equipped to handle the short-record problem, either the spatial resolution or the detection accuracy of the active regions can be improved by at least 50%.

Initial Estimates for Wiener-Hammerstein Models using the Best Linear Approximation

In this paper, a method is proposed to initialize the linear dynamic blocks of a Wiener-Hammerstein model. The idea is to build these blocks from the poles and the zeros of the best linear approximation of the system under test, which can easily be extracted from the data. This approach results in an easy to solve problem (linear-in-the-parameters) from which initial estimates for the linear dynamics can be obtained. The proposed method is applied to simulation data from a Wiener-Hammerstein system.

Monte-Carlo parameter uncertainty analysis under dynamical and operational measurement conditions

For controlling, observing and optimizing engineering processes one needs often dedicated experiments. Unfortunately no measurement is exact such that deriving conclusions from a measurement campaign requires some caution. Hence, in order to control or optimize a certain parameter of interest, uncertainty of the parameter needs to be the measurement quantified. In the literature two methods are proposed to perform this task: analysis of the noise propagation or Bootstrap Monte-Carlo (BMC) methods. The first one is inaccessible for the layman user. The BMC is difficult to perform if noise sources are mutually correlated since all correlations need to be taken into account. We present a new direct measurement for parameter uncertainty which can be operated under correlated noise sources without the need of explicit knowledge or description of the correlation at hand.

A Simple Nonparametric Preprocessing Technique to Correct for Nonstationary Effects in Measured Data

The general approach for modeling systems assumes that the measured signals are (weakly) stationary, i.e., the power spectrum is time invariant. However, the stationarity assumption is violated when: 1) transient effects due to experimental conditions are dominant; 2) data are missing due to, for instance, sensor failure; or 3) the amplitude of the excitation signals smoothly varies over time due to, for instance, actuator problems. Although different methods exist to deal with each of these nonstationary effects specifically, no unified approach is available. In this paper, a new and general technique is presented to handle nonstationary effects, based on processing overlapping subrecords of the measured data. The proposed method is a simple preprocessing step where the user does not need to specify which nonstationary effect is present, nor the time interval where the nonstationary effect appears. The merits of the proposed approach are demonstrated on an operational wireless system suffering from interrupted link effects.

Improved Variance Estimates of FRF Measurements in the Presence of Nonlinear Distortions Via Overlap

The frequency response function (FRF) is a common nonparametric modeling tool in many practical engineering problems used for obtaining insight in the device under test. However, the device often behaves nonlinearly. When nonlinearities are detected, the user wants to find out how large these are with respect to the measurement noise. In this paper, we describe a method, based on an overlap technique and periodic excitations, that accurately estimates the FRF, the level of nonlinear distortion, and the measurement noise using only two periods and two random phase realizations of the input signal.

Taylor–Fourier spectra to study fractional order systems

In measurement science mathematical models are often used as an indirect measurement of physical properties which are mapped to measurands through the mathematical model. Dynamical systems describing a physical process with a dominant diffusion or dispersion phenomenon requires a large dimensional model due to its long memory. Ignoring a dominant difussion or dispersion component acts as a confounder which may introduce a bias in the estimated quantities of interest. For linear systems it has been observed that fractional order models outperform classical rational forms in terms of the number of parameters for the same fitting error. However it is not straightforward to deal with a fractional order system or long memory effects without prior knowledge. Since the parametric modeling of a fractional system is very involved, we put forward the question whether fractional insight can be gathered in a non-parametric way. In this paper we show that classical Fourier basis leading to the frequency response function lacks fractional insight. To circumvent this problem, we introduce a fractional Taylor–Fourier basis to obtain non-parametric insight in the fractional system. This analysis proposes a novel type of spectrum to visualize the spectral content of a fractional system: Taylor–Fourier spectrum. This spectrum is fully measurement driven which can be used as a first to explore the fractional dynamics of a measured diffusion or dispersion system.

The use of the harmonic median for fMRI signal intensity characterization

The problem of detecting significant brain activity upon stimulus in functional Magnetic Resonance Imaging (fMRI) data is tackled by a statistical data analysis for which the signals amplitude is required. From literature, it is known that fMRI data follow a Rice distribution. Hence, for fMRI signal detection first the parameters of the Rice distribution need to be estimated. Different methods exist and each has their own pros and cons. In this paper, a novel estimation approach is presented in order to overcome the drawbacks of the existing methods. The proposed estimator is based on the harmonic median. Its performance is verified via a simulation experiment and compared with the state-of-the-art approaches.