In Magnetic Resonance Imaging (MRI), cardiac triggering that synchronizes data acquisition with cardiac contractions is an essential technique for acquiring high-quality images. Triggering is typically based on the Electrocardiogram (ECG) signal (e.g. R-peak). Since ECG acquisition involves extra workflow steps like electrode placement and ECG signals are usually disturbed by magnetic fields in high Magnetic Resonance (MR) systems, we explored camera-based photoplethysmography (PPG) as an alternative. We used the in-bore camera of a clinical MR system to investigate the feasibility and challenges of camera-based cardiac triggering. Data from ECG, finger oximeter and camera were synchronously collected. Compared to finger-PPG, camera-based PPG provides a higher availability of the signal and the PPG marker delay relative to the ECG R-peak is considerably less with a camera monitoring the forehead. The insights obtained in this study provide a basis for an envisioned system-design phase.

This paper introduces a powerful filtering method that exploits the physiological and optical properties of skin reflections to improve the performance of remote photoplethysmography (rPPG). Based on the fact that the pulsatile and nonpulsatile (e.g., intensity and specular changes) components have different reflection-spectra in a multi-wavelength camera, we propose to use their different characteristic color changes as a soft criterion to filter the RGB-signals in the frequency domain, such that the AC-components containing clear color distortions can be suppressed before the actual pulse extraction. This leads to a novel “Color-Distortion Filter” (CDF) that can be used as a common pre-processing step for arbitrary rPPG algorithms to increase their robustness. The benchmark in challenging fitness recordings shows that CDF brings significant and consistent improvements to all benchmarked rPPG algorithms, and drives all multi-channel approaches to a similar high quality-level.

With the growing amount of physical activity (PA) measures, the need for methods and algorithms that automatically analyze and interpret unannotated data increases. In this paper, PA is seen as a combination of multimodal constructs that can cooccur in different ways and proportions during the day. The design of a methodology able to integrate and analyze them is discussed, and its operation is illustrated by applying it to a dataset comprising data from COPD patients and healthy subjects acquired in daily life. The method encompasses different stages. The first stage is a completely automated method of labeling low-level multimodal PA measures. The information contained in the PA labels are further structured using topic modeling techniques, a machine learning method from the text processing community. The topic modeling discovers the main themes that pervade a large set of data. In our case, topic models discover PA routines that are active in the assessed days of the subjects under study. Applying the designed algorithm to our data provides new learnings and insights. As expected, the algorithm discovers that PA routines for COPD patients and healthy subjects are substantially different regarding their composition and moments in time in which transitions occur. Furthermore, it shows consistent trends relating to disease severity as measured by standard clinical practice.

Linear prediction has been successfully applied in many speech and audio processing systems. This paper presents the similarities and differences between two classes of linear prediction schemes, namely, Warped Linear Prediction (WLP) and Laguerre Linear Prediction (LLP). It is shown that both systems are closely related. In particular, we show that the LLP is in fact a WLP system where the optimization procedure is adapted such that the whitening property is automatically incorporated. The adaptation consists of a new linear constraint on the parameters. Furthermore, we show that an optimized WLP scheme where whitening is achieved by prefiltering before estimating the optimal coefficients results in a filter having all except the last reflection coefficient equal to those of the optimal LLP filter.

Linear prediction (LP) is a widely used technique in single-channel speech coding. LP can also be applied to stereo (or multi-channel) audio coding, but it requires a scheme for quantization and efficient transmission of stereo-LP parameters. We propose to transmit the (forward) normalized reflection matrices together with the zero-lag correlation matrix. Furthermore, we select a parameterization for the matrices and a quantization strategy per parameter. The parameterization for the normalized reflection matrices is a variant of the singular value decomposition (SVD). The proposed quantization scheme is evaluated for first-order stereo-LP systems. As criterion, we use a spectral distortion (SD) measure based on the norm of the transfer matrix of the synthesis filter. Simulations show that the distortions can easily be controlled as a function of bit rate

In Laguerre-based adaptive filtering, the pole can be used as an extra degree of freedom. An optimal pole is difficult to determine on the basis of a squared error criterion. A compaction criterion, on the other hand, does yield a simple explicit expression for the pole in terms of characteristics derived from the impulse response of the system. In this paper. it is shown how these characteristics can be calculated directly from a Laguerre expansion of the impulse response. For truncated Laguerre series, this implies a much faster algorithm than the one based on characteristics derived from the impulse response. Such an algorithm can be used to construct an adaptive Laguerre-based adaptive system, where the current estimated Laguerre expansion can be used to determine the pole in a next step. Simulation results on the behaviour of such a system for a synthetic signal are given in terms of signal cancellation and convergence speed.

This paper considers general, pure linear prediction schemes, where the prediction of the input signal is based on IIR-filtered versions of the one-sample-delayed input signal. Properties of these schemes are discussed, in particular, the whitening property and the realization and stability of the synthesis filter. In contrast to warped linear prediction, the synthesis filter can be realized in a way similar to the analysis filter. Furthermore, we prove that, at least for a specific class of systems, input data windowing for the calculation of the optimal prediction coefficients guarantees the stability of the synthesis filters. By simulation we show that the proposed prediction scheme, using properly parameterized Laguerre or Kautz systems, shows a behavior similar to that of warped linear prediction.

Kautz series allow orthogonal series expansion of finite-energy signals defined on a semi-infinite axis. The Kautz series consists of orthogonalized exponential functions or sequences. This series has as free parameters an ordered set of poles, each pole associated with an exponential function or sequence. For reasons of approximation and compact representation (coding), an appropriate set of ordered poles is therefore convenient. An iterative procedure to establish the optimal parameters according to an enforced convergence criterion is introduced.

In this paper we address the problem of approximating functions on a semi-infinite interval by truncated series of orthonormal generalized Laguerre functions. The generalized Laguerre functions contain two parameters, namely a scale factor and an order of generalization. The rate of convergence of a generalized Laguerre series depends on the choice of these parameters. Results concerning the determination of the two parameters are presented.

Adaptive filters can be made fault tolerant by overparametrization. Conditions are derived such that no deterioration is caused by the redundancy under fault-free operation and that the deterioration caused by weight failures is minimized.

A second-order IIR filter is considered as the basic component of an adaptive line enhancer (ALE). As a new feature, the bandwidth of the proposed ALE is adapted simultaneously with the center frequency. This leads to the possibility of combining the convergence speed and accuracy. The adaptation of the filter poles is controlled by a sign algorithm. The step sizes are chosen such that transients caused by the retuning of the filter are ensured to remain much smaller in amplitude than the response of the filter to the input signal. When the input signal consists of a sinusoid corrupted by wideband noise, an accurate frequency parameter estimate can be obtained with the algorithm given in the paper.