This paper presents the development of particleboard based on common reed, reproducing the industry standard manufacturing process applied to wood chipboard. One of the main properties of the ...resulting board was its resistance to water, due to the hydrophobic properties of the common reed, despite there being no incorporation of melamine or any other waterproofing additive. The boards that were developed were analyzed using 2 mm and 4 mm sieves for fibre selection, a manufacturing pressure of 3 N/mm2 and 25 N/mm2, and a volume of urea formaldehyde resin content ranging from 5.2% to 13% (8 to 20% liquid format). Standard destructive tests were performed. It was found that under certain applied conditions, namely high pressure and adequate resin proportion (a pressure of over 3 N/mm2 and over 15% liquid resin), Arundo donax L. particleboard demonstrated full recovery from the swelling test. This finding highlights an unmatched property in terms of recovery from the swelling test of the designed board. This property confers a interesting property to be used in high humidity environments without the need for special resin or waterproofing process.
New economic conditions have led to innovations in retail industries, such as more dynamic retail approaches based on flexible strategies. We propose and compare different approaches incorporating ...nonlinear methods for promotional decision-making using retail aggregated data registered at the point of the sale. Specifically, this paper describes a reliable quantification tool as an effective information system leveraged on recent and historical data that provides managers with an operative vision. Furthermore, a new set of indicators are proposed to evaluate the reliability and stability of the data model in the multidimensional feature space by using nonparametric resampling techniques. This allows the user to make a clearer comparison among linear, nonlinear, static, and dynamic data models, and to identify the uncertainty of different feature space regions, for example, corresponding to the most frequent deal features. This methodology allows retailers to use aggregated data in suitable conditions that will result in acceptable confidence intervals. To test the proposed methodology, we used a database containing the sales history of representative products registered by a Spanish retail chain. The results indicate that: (1) the deal effect curve analysis and the time series linear model do not provide enough expressive capacity, and (2) nonlinear promotional models more accurately follow the actual sales pattern obtained in response to the implemented sales promotions. The quarterly temporal analysis conducted enabled the authors to identify long-term changes in the dynamics of the model for several products, especially during the early stage of most recent economic crisis, consistent with the information provided by the reliability indices in terms of the feature space. We conclude that the proposed method provides a reliable operative tool for decision support, allowing retailers to alter their strategies to accommodate consumer behavior.
Cryo-ablation is a common procedure used in hospitals to eliminate certain arrhythmia, such as Atrial Fibrillation. This procedure, sometimes, involves treatments in areas close to the phrenic nerve ...with the subsequent risk of later damage to the aforementioned nerve. To avoid this, clinical practice incorporates manual safety protocols during ablation. We propose the development of an automated classifier that facilitates the clinical evaluation of possible conduction disorders produced in the phrenic nerve. To achieve this goal, polygraph signals extracted during the ablation process of ten patients were used. To unmask the residue of cellular muscle potential during the phrenic nerve stimulation process we compare utilizing signal processing the results when the sensor was placed on the phrenic nerve (activation capture) and when the sensor was displaced from the phrenic nerve (no capture). A linear classifier was applied to both situations to characterize muscle activity resulting from nerve activation. The results confirmed that it is possible to automatically classify the level of muscle activity from the phrenic nerve with 100% accuracy in this data set. The method proposed in this work constitutes an automated protocol to evaluate the eventual deterioration of the phrenic nerve conduction due to ablation in the vicinity, improving the existing protocol for clinical convenience.
Simulations of bioelectric potentials in the direct problem usually require numerical integration in the spatial dimensions to obtain both the transmembrane current diffusion and the extracellular ...potentials in homogeneous conductivity conditions. Given that the Laplacian of a potential field in said conditions is a spatially linear operator, we propose its implementation with non-uniformly spaced point clouds using a static-matrix formulation which avoids the integration in the spatial domain. We also analyzed the effect of severe irregular sampling in these calculations. Matrix estimators in the 3-dimensional space were built for the Laplacian operator in point clouds defining lines and surfaces. An optimized algorithm was proposed, in which the spatial convolution of the Laplacian impulse response is locally and globally estimated using convex programming techniques, sparse matrix representations, and basic concepts of Graph Theory. We benchmarked the behavior of the estimated synthetic transmembrane and extracellular potentials with simple geometrical substrates. Our proposal paves the way towards simplifying spatial evolution in computer simulations and its use in more clinically realistic environments, such as non-homogeneous conductivity in volume conductor problems and patient-based arrhythmia simulations from point clouds in Electrophysiology Laboratory.
Phospholamban (PLN) p.Arg14del mutation carriers are known to develop dilated and/or arrhythmogenic cardiomyopathy, and typical electrocardiographic (ECG) features have been identified for diagnosis. ...Machine learning is a powerful tool used in ECG analysis and has shown to outperform cardiologists.
We aimed to develop machine learning and deep learning models to diagnose PLN p.Arg14del cardiomyopathy using ECGs and evaluate their accuracy compared to an expert cardiologist.
We included 155 adult PLN mutation carriers and 155 age- and sex-matched control subjects. Twenty-one PLN mutation carriers (13.4%) were classified as symptomatic (symptoms of heart failure or malignant ventricular arrhythmias). The data set was split into training and testing sets using 4-fold cross-validation. Multiple models were developed to discriminate between PLN mutation carriers and control subjects. For comparison, expert cardiologists classified the same data set. The best performing models were validated using an external PLN p.Arg14del mutation carrier data set from Murcia, Spain (n = 50). We applied occlusion maps to visualize the most contributing ECG regions.
In terms of specificity, expert cardiologists (0.99) outperformed all models (range 0.53–0.81). In terms of accuracy and sensitivity, experts (0.28 and 0.64) were outperformed by all models (sensitivity range 0.65–0.81). T-wave morphology was most important for classification of PLN p.Arg14del carriers. External validation showed comparable results, with the best model outperforming experts.
This study shows that machine learning can outperform experienced cardiologists in the diagnosis of PLN p.Arg14del cardiomyopathy and suggests that the shape of the T wave is of added importance to this diagnosis.
Hypertrophic Cardiomyopathy (HCM) consists of a thickening of the cardiac muscle, causing fatigue, changes in the cardioelectric system, arrhythmias, and even sudden deaths. Variants in gene MYBPC3 ...are a well-known cause of this illness. Our objective was to find variants in other genes that can cause this pathology. For that purpose, genetic data from a group of patients is analyzed using embedding methods, which allow a lower dimensional representation, which is very helpful for visualization, diagnosis, and personalized therapy. Our results, applying different methods -Principal Component Analyisis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), Orthonormalized Partial Least Squares (OPLS) and Supervised Autoencoders- on genetic data showed a very good separability in the embedded space, allowing us to identify 10 variants that cause that separability. These results may be useful for identifying new HCM cases and implementing new Machine Learning models in those embedded spaces.
Heart rate turbulence (HRT) has been successfully explored for cardiac risk stratification. While HRT is known to be influenced by the heart rate (HR) and the coupling interval (CI), non-concordant ...results have been reported on how the CI influences HRT. The purpose of this study is to investigate HRT changes in terms of CI and HR by means of a specially designed protocol.
A dataset was acquired from 11 patients with structurally normal hearts for which CI was altered by different pacing trains and HR by isoproterenol during electrophysiological study (EPS). The protocol was designed so that, first, the effect of HR changes on HRT, and, second, the combined effect of HR and CI could be explored. As a complement to the EPS dataset, a database of 24-h Holters from 61 acute myocardial infarction (AMI) patients was studied for the purpose of assessing risk. Data analysis was performed by using different nonlinear ridge regression models, and the relevance of model variables was assessed using resampling methods. The EPS subjects, with and without isoproterenol, were analyzed separately.
The proposed nonlinear regression models were found to account for the influence of HR and CI on HRT, both in patients undergoing EPS without isoproterenol and in low-risk AMI patients, whereas this influence was absent in highrisk AMI patients. Moreover, model coefficients related to CI were not statistically significant, p > 0:05, on EPS subjects with isoproterenol.
The observed relationship between CI and HRT, being in agreement with the baroreflex hypothesis, was statistically significant (p < 0:05), when decoupling the effect of HR and normalizing the CI by the HR.
The results of this work can help to provide new risk indicators that take into account physiological influence on HRT, as well as to model how this influence changes in different cardiac conditions.
In the past few years, the presence of fragmentation in the QRS complex has been demonstrated to be related to diseases such as myocardial fibrosis, cardiac sarcoidosis, arrythmogenic cardiopathies, ...acute coronary syndrome, and Brugada syndrome, among others. The detection of fragmentation in the QRS is usually carried out manually, which represents a subjective pattern recognition task that demands an effort by the clinician, increasing with the number of patients. These problems have made the process of fragmentation detection a good candidate to its automatization. In this work, we used a database with over six-thousand 12-lead ECG from Hospital Virgen de la Arrixaca de Murcia (Spain), which where digitally recorded with GE MAC5000. Affected and non-affected patients records were extracted for computerized analysis. Clinical supervision was performed for gold-standard development and for signal classification. Fragmentation detection algorithms were developed using first and second derivatives calculation in the pre-qualified segments of the signal, after fiducial point detection. The obtained results were 96.88% sensitivity, 72.92% specificity, and 82.50% accuracy. These results confirm that it is possible to automatically detect fragmentation, constituting a relevant tool to pre-qualify patients for further diagnostic-tests, and it also opens new opportunities for computerized diagnosis.
The tilt test is a valuable clinical tool for the diagnosis of Vasovagal Syncope. No practical system has been implemented to predict the tilt test outcome at the beginning in the procedure. Our ...objective was to evaluate and benchmark, over a sufficient database, the predictive performance of the proposed parameters in the literature. We analyzed a database of 727 consecutive cases of tilt test. Previously proposed features were measured from heart rate and systolic/diastolic pressure, in several representative signal segments. A support vector machine (SVM) was used to predict the test outcome with the available features. Also the inclusion of additional physiological signals (impedance) was intended to improve the performance. The predictive performance of the nonline-arly combined previously proposed features was limited (p<;0.03 and area under ROC curve 0.57±0.12), especially in the beginning of the test, which is the most clinically relevant period. The improvement with additional available physiological information and SVM was limited (area under ROC curve 0.59±0.22). We conclude that the existing methods for tilt test outcome prediction knowledge should be considered with caution.
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