Archeologisch onderzoek in de Hoogstraat te Mechelen, zone A (prov. Antwerpen). Eindrapport

Dit rapport werd ingediend bij het agentschap samen met een aantal afzonderlijke digitale bijlagen. Een aantal van deze bijlagen zijn niet inbegrepen in dit pdf document en zijn niet online beschikbaar. Sommige bijlagen (grondplannen, fotos, spoorbeschrijvingen, enz.) kunnen van belang zijn voor een betere lezing en interpretatie van dit rapport. Indien u deze bijlagen wenst te raadplegen kan u daarvoor contact opnemen met: [email protected]

Tensor-based Analysis of ECG changes prior to in-hospital cardiac arrest

© 2017 IEEE Computer Society. All rights reserved. This works presents an analysis in the changes in beat morphology prior to in-hospital cardiac arrest. We have used tensor decomposition methods to extract features from the ECG signal. After preprocessing and R peak detection, a tensor is constructed for each ECG signal by segmenting the signal in individual heartbeats and stacking them in a 3D manner. The result of the tensor decomposition are 3 factor vectors corresponding to each tensor dimension. The temporal vector, representing the standard heartbeat over all leads in the signal, is further processed to calculate 10 different features: 4 features characterizing global changes in beat morphology and 6 detailed features describing changes in timing and amplitude of the waveforms. We analyzed a dataset of 20 patients who experienced a cardiac arrest in the intensive care unit at the end of the recording. For each patient, a stable signal (in the beginning of the recording) and an unstable signal (near the cardiac arrest) were extracted and processed. Statistical analysis of the results in both time windows (e.g. stable and unstable) show significant changes in the values of 2 out of 4 global parameters and 4 out of 6 detailed parameters. The results indicate that the use of tensor-based methods can be a robust way to characterize ECG changes, and may be a useful tool in identifying patients at risk for cardiac arrest.status: publishe

Automatic detection of T wave alternans using tensor decompositions in multilead ECG signals

OBJECTIVE: T wave alternans (TWA) is a promising non-invasive risk stratification tool for sudden cardiac death which can be detected from surface ECG. This paper proposes a novel method to automatically detect TWA based on tensor decomposition methods. APPROACH: Two different tensor decomposition approaches are examined and compared, namely canonical polyadic decomposition and the more generalized variation PARAFAC2 which allows the T waves to shift in time. RESULTS AND SIGNIFICANCE: Results on different artificial and clinical signals show that the presented methods are a robust and reliable way for TWA detection, and show the potential benefit of tensors in ECG signal processing.status: publishe

Tensor-based Detection of T Wave Alternans in Multilead ECG Signals

In this study, a new method for the detection of T wave alternans in multichannel ECG signals is introduced. The use of tensors (multidimensional matrices) allows us to combine the information present in all channels, making detection more robust. To construct a 3D tensor from a 2D ECG signal, the T wave is first roughly segmented. The intervals are then placed after each other to obtain a 3D structure with dimensions time, space and heartbeats. The tensor is decomposed using Canonical Polyadic Decomposition. The result is 1 rank-one tensor consisting of 3 loading vectors (which match the 3 dimensions of the original tensor). The third loading vector corresponds to the heartbeats dimension and gives information about the behavior of the T wave in different heartbeats. The Fourier transform of this loading vector can then be used to examine the presence of TWA. The methods have been tested on a subset of the T wave alternans database available on Physionet. Results show a very clear distinction between loading vectors of signals from both groups: the power of the loading vector in the TWA group is on average 100 times larger than in the control group. This suggests that tensors are an effective way of detecting TWA in multilead signals.status: publishe

Detection of Irregular Heartbeats using tensors

© 2015 CCAL. Automatic classification of heartbeats in different categories is important for ECG analysis. The number of irregular heartbeats in a signal can for example be used as a risk stratifier for sudden cardiac death. Current heart-beat classification methods typically use time or frequency domain features to characterize heartbeats. We propose the use of tensors to incorporate the structural information that is present in multilead ECG channels. Since different ECG leads provide information on a particular orientation in space, more robust detection can be done if all leads are considered. After preprocessing and heartbeat detection using wavelet-based methods, the ECG signal is segmented beat-by-beat. The different heartbeats are then placed in a three-dimensional tensor with dimensions time, channels and heartbeats. Canonical Polyadic Decomposition is used to decompose the tensor. The results are three loading vectors, corresponding to the dimensions of the original tensor. Through analysis of these loading vectors, irregular heartbeats can be detected using a simple thresholding procedure. The method has been applied to a subset of the St.-Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database available on Physionet. When applying the method to the first 10 signals, we obtain a mean sensitivity and specificity of more than 90%. These results indicate that the presented method is a new and reliable way of performing irregular heartbeat detection.status: publishe

The Power of Tensor-Based Approaches in Cardiac Applications

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Optical and EPR spectroscopy in pure and blended films of a novel low band gap polymer

Efficient charge transfer between a newly synthesized low band gap (LBG) conjugated polymer and the C60 derivative PCBM is demonstrated. Spectral and time-resolved photoluminescence (PL) measurements show strong quenching and charge separation in the picosecond time range. The charge transfer is further confirmed by light-induced electron paramagnetic resonance (EPR) measurements of both the positive polarons and the fullerene radicals, which can be resolved in high-frequency EPR. The potential for the LBG polymer to act as acceptor was examined in blends with the para-phenylene-vinylene polymer MDMO-PPV. No charge transfer, but instead energy transfer from MDMO-PPV to LBG occurs in these blends, as shown by PL spectroscopy

Heart Beat Detection in Multimodal Data Using Signal Recognition and Beat Location Estimation

The tachogram is typically constructed by detecting the R peaks in the electrocardiogram (ECG). Sometimes the ECG is however very noisy, which makes it hard to find the R peaks in these cases by using only the ECG. Information from other signals can then be used in order to find the R peaks. In this paper, a method is suggested that is able to automatically detect signals with the same periodic behavior as the ECG. Heart beat labels of the detected signals are combined by using majority voting, heart beat location estimation and Hjorth's mobility parameter. The average performance was 99.95% for the training set and 85.62% for the last phase of the 2014 Computing in Cardiology challenge. If the available labels for the signals are used, the performance on the hidden test set was 86.61%.status: publishe