Jwm Jan Bergmans

An Adaptive Kalman Filter for ECG Signal Enhancement

The ongoing trend of ECG monitoring techniques to become more ambulatory and less obtrusive generally comes at the expense of decreased signal quality. To enhance this quality, consecutive ECG complexes can be averaged triggered on the heartbeat, exploiting the quasi-periodicity of the ECG. However, this averaging constitutes a tradeoff between improvement of the SNR and loss of clinically relevant physiological signal dynamics. Using a Bayesian framework, in this paper, a sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the averaging based on the characteristics of the ECG signal. The filter has the form of an adaptive Kalman filter. The adaptive estimation of the process and measurement noise covariances is performed by maximizing the Bayesian evidence function of the sequential ECG estimation and by exploiting the spatial correlation between several simultaneously recorded ECG signals, respectively. The noise covariance estimates thus obtained render the filter capable of ascribing more weight to newly arriving data when these data contain morphological variability, and of reducing this weight in cases of no morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To gauge the relevance of the adaptive noise-covariance estimation, the performance of the filter is compared to that of a Kalman filter with fixed, (a posteriori) optimized noise covariance. This comparison demonstrates that, without using a priori knowledge on signal characteristics, the filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance, favoring the adaptive filter in cases where no a priori information is available or where signal characteristics are expected to fluctuate.

Estimation of internal uterine pressure by joint amplitude and frequency analysis of electrohysterographic signals

Monitoring the uterine contraction provides important prognostic information during pregnancy and parturition. The existing methods employed in clinical practice impose a compromise between reliability and invasiveness. A promising technique for uterine contraction monitoring is electrohysterography (EHG). The EHG signal measures the electrical activity which triggers the contraction of the uterine muscle. In this paper, a non-invasive method for intrauterine pressure (IUP) estimation by EHG signal analysis is proposed. The EHG signal is regarded as a non-stationary signal whose frequency and amplitude characteristics are related to the IUP. After acquisition in a multi-channel configuration, the EHG signal is therefore analyzed in the time-frequency domain. A first estimation of the IUP is then derived by calculation of the unnormalized first statistical moment of the frequency spectrum. The estimation accuracy is finally increased by identification of a second-order polynomial model. The proposed method is compared to root mean squared analysis and optimal linear filtering and validated by simultaneous measurement of the IUP on nine women during labor. The results suggest that the proposed EHG signal analysis provides an accurate estimate of the IUP.

Pilot-Aided Angle-Domain Channel Estimation Techniques for MIMO-OFDM Systems

Early multiple-input multiple-output with orthogonal frequency division multiplexing (MIMO-OFDM) channel estimation techniques treat channels as spatially uncorrelated. However, in many situations, MIMO-OFDM channels tend to be spatially correlated, for example, due to limited scattering. For such channels, estimation performance can be improved through exploitation of prior knowledge of the channel spatial correlation, for example, by means of the linear multiple mean square error (MMSE) technique. This knowledge is, however, not always available. As an alternative, we investigate techniques in the angle domain, where the MIMO-OFDM channel model lends itself to a physical interpretation. Our theoretical analysis and simulation results indicate that the proposed angle-domain approximated MMSE (AMMSE) channel estimation technique performs well in terms of the mean square error (mse) for various channel models representing different indoor environments. When a suitable threshold is chosen, we can use the angle-domain most-significant-taps selection technique instead of the angle-domain AMMSE technique to simplify the channel estimation procedure with little performance loss.

Illumination Sensing in LED Lighting Systems Based on Frequency-Division Multiplexing

Recently, light emitting diode (LED) based illumination systems have attracted considerable research interest. Such systems normally consist of a large number of LEDs. In order to facilitate the control of such high-complexity system, a novel signal processing application, namely illumination sensing, is thus studied. In this paper, the system concept and research challenges of illumination sensing are presented. Thereafter, we investigate a frequency-division multiplexing (FDM) scheme to distinguish the signals from different LEDs, such that we are able to estimate the illuminances of all the LEDs simultaneously. Moreover, a filter bank sensor structure is proposed to study the key properties of the FDM scheme. Conditions on the design of the filter response are imposed for the ideal case without the existence of any frequency inaccuracy, as well as for the case with frequency inaccuracies. The maximum number of LEDs that can be supported for each case is also derived. In particular, it is shown that, among all the other considered functions, the use of the triangular function is able to give a better tradeoff between the number of LEDs that can be supported and the allowable clock inaccuracies within a practical range. Moreover, through numerical investigations, we show that many tens of LEDs can be supported for the considered system parameters. Remark on the low-cost implementations of the proposed sensor structure is also provided.

Using an Eye Tracker for Accurate Eye Movement Artifact Correction

We present a new method to correct eye movement artifacts in electroencephalogram (EEG) data. By using an eye tracker, whose data cannot be corrupted by any electrophysiological signals, an accurate method for correction is developed. The eye-tracker data is used in a Kalman filter to estimate which part of the EEG is of ocular origin. The main assumptions for optimal correction are summed and their validity is proven. The eye-tracker-based correction method is objectively evaluated on simulated data of four different types of eye movements and visually evaluated on experimental data. Results are compared to three established correction methods: regression, principal component analysis, and second-order blind identification. A comparison of signal to noise ratio after correction by these methods is given in Table II and shows that our method is consistently superior to the other three methods, often by a large margin. The use of a reference signal without electrophysiological influences, as provided by an eye tracker, is essential to achieve optimal eye movement artifact removal.

Inter-electrode delay estimators for electrohysterographic propagation analysis

Premature birth is a major cause of mortality and permanent dysfunctions. Several parameters derived from single channel electrohysterographic (EHG) signals have been considered to determine contractions leading to preterm delivery. The results are promising, but improvements are needed. As effective uterine contractions result from a proper action potential propagation, in this paper we focus on the propagation properties of EHG signals, which can be predictive of preterm delivery. Two standard delay estimators, namely maximization of the cross-correlation function and spectral matching, are adapted and implemented for the assessment of inter-electrode delays of propagating EHG signals. The accuracy of the considered standard estimators might be hampered by a poor inter-channel correlation. An improved dedicated approach is therefore proposed. By simultaneous adaptive estimation of the volume conductor transfer function and the delay, a dedicated method is conceived for improving the inter-channel signal similarity during delay calculation. Furthermore, it provides delay estimates without resolution limits and it is suitable for low sampling rates, which are appropriate for EHG recording. The three estimators were evaluated on EHG signals recorded on seven women. The dedicated approach provided more accurate estimates due to a 22% improvement of the initial average inter-channel correlation.

Modeling and Identification of the Electrohysterographic Volume Conductor by High-Density Electrodes

The surface electrohysterographic (EHG) signal represents the bioelectrical activity that triggers the mechanical contraction of the uterine muscle. Previous work demonstrated the relevance of the EHG signal analysis for fetal and maternal monitoring as well as for prognosis of preterm labor. However, for the introduction in the clinical practice of diagnostic and prognostic EHG techniques, further insights are needed on the properties of the uterine electrical activation and its propagation through biological tissues. An important contribution for studying these phenomena in humans can be provided by mathematical modeling. A five-parameter analytical model of the EHG volume conductor and the cellular action potential (AP) is proposed here and tested on EHG signals recorded by a grid of 64 high-density electrodes. The model parameters are identified by a least-squares optimization method that uses a subset of electrodes. The parameters representing fat and abdominal muscle thickness are also measured by echography. The mean correlation coefficient and standard deviation of the difference between the echographic and EHG estimates were 0.94 and 1.9 mm, respectively. No bias was present. These results suggest that the model provides an accurate description of the EHG AP and the volume conductor, with promising perspectives for future applications.

Heart Rate Detection in Low Amplitude Non-Invasive Fetal ECG Recordings

Multi-electrode electrical measurements on the maternal abdomen may provide a valuable alternative to standard fetal monitoring. Removal of the maternal ECG from these recordings by means of subtracting a weighted linear combination of segments from preceding maternal ECG complexes, results in fetal ECG traces from which the fetal heart rate can be determined. Unfortunately, these traces often contain too much noise to determine the heart rate by R-peak detection. To overcome this limitation, an algorithm has been developed that calculates the heart rate based on cross-correlation. To validate the algorithm, noise was added to a fetal scalp ECG recording to simulate low amplitude abdominal recordings. Heart rates calculated by the algorithm were compared to the heart rates from the original scalp ECG. For simulated signals with a signal to noise ratio of 2, the coefficient of correlation was 0.99 (p<0.001). By using the developed algorithm for calculating the fetal heart rate, multi-electrode electrical measurements on the maternal abdomen now can be used for fetal monitoring in relatively early stages of pregnancy or other situations where ECG amplitudes are low or noise levels are high

Maternal ECG removal from non-invasive fetal ECG recordings

Fetal monitoring during pregnancy is important to support medical decision making. The fetal electrocardiogram (fECG) is a valuable signal to diagnose fetal well-being. Non-invasive recording of the fECG is performed by positioning electrodes on the maternal abdomen. The signal to noise ratio of these recordings is relatively low and the main undesired signal is the maternal electrocardiogram (mECG). Existing methods to remove the mECG signal are not sufficiently accurate to extract the complete fECG signal. In this paper, a novel method for removal of the mECG signal from abdominal recordings is presented. It is an extension of the linear prediction method. Each mECG complex is segmented and these segments are separately estimated by linear prediction. Both the presented method and the standard linear prediction are applied to simulated abdominal recordings and evaluated by determining the rms errors between the estimated and the actual fECG signals. The ratio between the rms errors of the linear prediction method and the presented method varies between 0.4 dB and 2.3 dB. It can therefore be concluded that the presented method is capable of a more accurate removal of the mECG signal for all simulated abdominal recordings with respect to the linear prediction method

An investigation of the phase locking index for measuring of interdependency of cortical source signals recorded in the EEG

The phase locking index (PLI) was introduced to quantify in a statistical sense the phase synchronization of two signals. It has been commonly used to process biosignals. In this article, we investigate the PLI for measuring the interdependency of cortical source signals (CSSs) recorded in the Electroencephalogram (EEG). To this end, we consider simple analytical models for the mapping of simulated CSSs into the EEG. For these models, the PLI is investigated analytically and through numerical simulations. An evaluation is made of the sensitivity of the PLI to the amount of crosstalk between the sources through biological tissues of the head. It is found that the PLI is a useful interdependency measure for CSSs, especially when the amount of crosstalk is small. Another common interdependency measure is the coherence. A direct comparison of both measures has not been made in the literature so far. We assess the performance of the PLI and coherence for estimation and detection purposes based on, respectively, a normalized variance and a novel statistical measure termed contrast. Based on these performance measures, it is found that the PLI is similar or better than the CM in most cases. This result is also confirmed through analysis of EEGs recorded from epileptic patients.