Electrohysterogram signal component cataloging with spectral and time-frequency methods

The Electrohysterogram (EHG) is a new instrument for pregnancy monitoring. It measures the uterine muscle electrical signal, which is closely related with uterine contractions. The EHG is described as a viable alternative and a more precise instrument than the currently most widely used method for the description of uterine contractions: the external tocogram. The EHG has also been indicated as a promising tool in the assessment of preterm delivery risk. This work intends to contribute towards the EHG characterization through the inventory of its components which are: • Contractions; • Labor contractions; • Alvarez waves; • Fetal movements; • Long Duration Low Frequency Waves; The instruments used for cataloging were: Spectral Analysis, parametric and non-parametric, energy estimators, time-frequency methods and the tocogram annotated by expert physicians. The EHG and respective tocograms were obtained from the Icelandic 16-electrode Electrohysterogram Database. 288 components were classified. There is not a component database of this type available for consultation. The spectral analysis module and power estimation was added to Uterine Explorer, an EHG analysis software developed in FCT-UNL. The importance of this component database is related to the need to improve the understanding of the EHG which is a relatively complex signal, as well as contributing towards the detection of preterm birth. Preterm birth accounts for 10% of all births and is one of the most relevant obstetric conditions. Despite the technological and scientific advances in perinatal medicine, in developed countries, prematurity is the major cause of neonatal death. Although various risk factors such as previous preterm births, infection, uterine malformations, multiple gestation and short uterine cervix in second trimester, have been associated with this condition, its etiology remains unknown [1][2][3].

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

Power quality studies in distribution systems involving spectral decomposition

Distribution systems, eigenvalue analysis, nodal admittance matrix, power quality, spectral decomposition

Spectral Analysis Related to Bare-Metal and Drug-Eluting Coronary Stent Implantation

Background: The autonomic nervous system plays a central role in cardiovascular regulation; sympathetic activation occurs during myocardial ischemia. Objective: To assess the spectral analysis of heart rate variability during stent implantation, comparing the types of stent. Methods: This study assessed 61 patients (mean age, 64.0 years; 35 men) with ischemic heart disease and indication for stenting. Stent implantation was performed under Holter monitoring to record the spectral analysis of heart rate variability (Fourier transform), measuring the low-frequency (LF) and high-frequency (HF) components, and the LF/HF ratio before and during the procedure. Results: Bare-metal stent was implanted in 34 patients, while the others received drug-eluting stents. The right coronary artery was approached in 21 patients, the left anterior descending, in 28, and the circumflex, in 9. As compared with the pre-stenting period, all patients showed an increase in LF and HF during stent implantation (658 versus 185 ms2, p = 0.00; 322 versus 121, p = 0.00, respectively), with no change in LF/HF. During stent implantation, LF was 864 ms2 in patients with bare-metal stents, and 398 ms2 in those with drug-eluting stents (p = 0.00). The spectral analysis of heart rate variability showed no association with diabetes mellitus, family history, clinical presentation, beta-blockers, age, and vessel or its segment. Conclusions: Stent implantation resulted in concomitant sympathetic and vagal activations. Diabetes mellitus, use of beta-blockers, and the vessel approached showed no influence on the spectral analysis of heart rate variability. Sympathetic activation was lower during the implantation of drug-eluting stents.

On the homotopy theory of spectral categories

"Vegeu el resum a l'inici del document del fitxer adjunt."

Spectral analysis of random sparse matrices

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Relating the permeability of quartz sands to their grain size and spectral induced polarization characteristics

Recently, Revil & Florsch proposed a novel mechanistic model based on the polarization of the Stern layer relating the permeability of granular media to their spectral induced polarization (SIP) characteristics based on the formation of polarized cells around individual grains. To explore the practical validity of this model, we compare it to pertinent laboratory measurements on samples of quartz sands with a wide range of granulometric characteristics. In particular, we measure the hydraulic and SIP characteristics of all samples both in their loose, non-compacted and compacted states, which might allow for the detection of polarization processes that are independent of the grain size. We first verify the underlying grain size/permeability relationship upon which the model of Revil & Florsch is based and then proceed to compare the observed and predicted permeability values for our samples by substituting the grain size characteristics by corresponding SIP parameters, notably the so-called Cole-Cole time constant. In doing so, we also asses the quantitative impact of an observed shift in the Cole-Cole time constant related to textural variations in the samples and observe that changes related to the compaction of the samples are not relevant for the corresponding permeability predictions. We find that the proposed model does indeed provide an adequate prediction of the overall trend of the observed permeability values, but underestimates their actual values by approximately one order-of-magnitude. This discrepancy in turn points to the potential importance of phenomena, which are currently not accounted for in the model and which tend to reduce the characteristic size of the prevailing polarization cells compared to the considered model, such as, for example, membrane polarization, contacts of double-layers of neighbouring grains, and incorrect estimation of the size of the polarized cells because of the irregularity of natural sand grains.

On spectral properties of the modified convolution operator

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Krein's strings whose spectral functions are of polynomial growth

In case Krein's strings with spectral functions of polynomial growth a necessary and su fficient condition for the Krein's correspondence to be continuous is given.

Weighted Logratio Biplots, Correspondence Analysis and Spectral Maps

Starting with logratio biplots for compositional data, which are based on the principle of subcompositional coherence, and then adding weights, as in correspondence analysis, we rediscover Lewi's spectral map and many connections to analyses of two-way tables of non-negative data. Thanks to the weighting, the method also achieves the property of distributional equivalence

Evaluation of a linear spectral mixture model and vegetation indices (NDVI and EVI) in a study of schistosomiasis mansoni and Biomphalaria glabrata distribution in the state of Minas Gerais, Brazil

This paper analyses the associations between Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) on the prevalence of schistosomiasis and the presence of Biomphalaria glabrata in the state of Minas Gerais (MG), Brazil. Additionally, vegetation, soil and shade fraction images were created using a Linear Spectral Mixture Model (LSMM) from the blue, red and infrared channels of the Moderate Resolution Imaging Spectroradiometer spaceborne sensor and the relationship between these images and the prevalence of schistosomiasis and the presence of B. glabrata was analysed. First, we found a high correlation between the vegetation fraction image and EVI and second, a high correlation between soil fraction image and NDVI. The results also indicate that there was a positive correlation between prevalence and the vegetation fraction image (July 2002), a negative correlation between prevalence and the soil fraction image (July 2002) and a positive correlation between B. glabrata and the shade fraction image (July 2002). This paper demonstrates that the LSMM variables can be used as a substitute for the standard vegetation indices (EVI and NDVI) to determine and delimit risk areas for B. glabrata and schistosomiasis in MG, which can be used to improve the allocation of resources for disease control.