Parameterization and R-Peak Error Estimations of ECG Signals Using Independent Component Analysis

Principal component analysis (PCA) is used to reduce dimensionality of electrocardiogram (ECG) data prior to performing independent component analysis (ICA). A newly developed PCA variance estimator by the author has been applied for detecting true, actual and false peaks of ECG data files. In this paper, it is felt that the ability of ICA is also checked for parameterization of ECG signals, which is necessary at times. Independent components (ICs) of properly parameterized ECG signals are more readily interpretable than the measurements themselves, or their ICs. The original ECG recordings and the samples are corrected by statistical measures to estimate the noise statistics of ECG signals and find the reconstruction errors. The capability of ICA is justified by finding the true, false and actual peaks of around 25–50, CSE (common standards for electrocardiography) database ECG files. In the present work, joint approximation for diagonalization of the eigen matrices (Jade) algorithm is applied to 3-channel ECG. ICA processing of different cases is dealt with and the R-peak magnitudes of the ECG waveforms before and after applying ICA are found and marked. ICA results obtained indicate that in most of the cases, the percentage error in reconstruction is very small. The developed PCA variance estimator along with the quadratic spline wavelet gave a sensitivity of 97.47% before applying ICA and 98.07% after ICA processing

Deep Over-sampling Framework for Classifying Imbalanced Data

Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose Deep Over-sampling (DOS), a framework for extending the synthetic over-sampling method to exploit the deep feature space acquired by a convolutional neural network (CNN). Its key feature is an explicit, supervised representation learning, for which the training data presents each raw input sample with a synthetic embedding target in the deep feature space, which is sampled from the linear subspace of in-class neighbors. We implement an iterative process of training the CNN and updating the targets, which induces smaller in-class variance among the embeddings, to increase the discriminative power of the deep representation. We present an empirical study using public benchmarks, which shows that the DOS framework not only counteracts class imbalance better than the existing method, but also improves the performance of the CNN in the standard, balanced settings

VITO combineert sensorplatformen met aardobservatie voor een betere monitoring van water

De huidige systemen om de toestand van het water op te volgen, voldoen vaak niet aan de noden van waterbeheerders, baggeraars, waterbedrijven, havenbeheerders, enzovoort. De data schieten tekort in kwaliteit en kwantiteit. Daarom ontwikkelt VITO een monitoringssysteem dat geautomatiseerde sensoren op onbemande vaartuigen combineert met aardobservatie: SAVEWATER. Ook het beschikbaar stellen van de data maakt deel uit van dit systeem. Het project wordt samen met de Europese ruimtevaartorganisatie ESA uitgewerkt

A critical look at studies applying over-sampling on the TPEHGDB dataset

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set

Effect of target material on fast-electron transport and resistive collimation.

The effect of target material on fast-electron transport is investigated using a high-intensity (0.7 ps, ${10}^{20}\text{ }\text{ }\mathrm{W}/{\mathrm{cm}}^{2}$) laser pulse irradiated on multilayered solid Al targets with embedded transport (Au, Mo, Al) and tracer (Cu) layers, backed with millimeter-thick carbon foils to minimize refluxing. We consistently observed a more collimated electron beam (36% average reduction in fast-electron induced Cu K\ensuremath{\alpha} spot size) using a high- or mid-$Z$ (Au or Mo) layer compared to Al. All targets showed a similar electron flux level in the central spot of the beam. Two-dimensional collisional particle-in-cell simulations showed formation of strong self-generated resistive magnetic fields in targets with a high-$Z$ transport layer that suppressed the fast-electron beam divergence; the consequent magnetic channels guided the fast electrons to a smaller spot, in good agreement with experiments. These findings indicate that fast-electron transport can be controlled by self-generated resistive magnetic fields and may have important implications to fast ignition

Improving Attitude Words Classification for Opinion Mining using Word Embedding

[EN] Recognizing and classifying evaluative expressions is an important issue of sentiment analysis. This paper presents a corpus-based method for classifying attitude types (Affect, Judgment and Appreciation) and attitude orientation (positive and negative) of words in Spanish relying on the Attitude system of the Appraisal Theory. The main contribution lies in exploring large and unlabeled corpora using neural network word embedding techniques in order to obtain semantic information among words of the same attitude and orientation class. Experimental results show that the proposed method achieves a good effectiveness and outperforms the state of the art for automatic classification of attitude words in Spanish language.The work of the fourth author was partially supported by the SomEMBED TIN2015-71147-C2-1-P research project (MINECO/FEDER).Ortega-Bueno, R.; Medina-Pagola, JE.; Muñiz-Cuza, CE.; Rosso, P. (2019). Improving Attitude Words Classification for Opinion Mining using Word Embedding. Lecture Notes in Computer Science. 11401:971-982. https://doi.org/10.1007/978-3-030-13469-3_112S9719821140