Correction: Real Time QRS Detection Based on M-ary Likelihood Ratio Test on the DFT Coefficients
Ontology highlight
ABSTRACT:
SUBMITTER:
PROVIDER: S-EPMC4277461 | biostudies-literature | 2014 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:This paper shows an adaptive statistical test for QRS detection of electrocardiography (ECG) signals. The method is based on a M-ary generalized likelihood ratio test (LRT) defined over a multiple observation window in the Fourier domain. The motivations for proposing another detection algorithm based on maximum a posteriori (MAP) estimation are found in the high complexity of the signal model proposed in previous approaches which i) makes them computationally unfeasible or not intended for real time applications such as intensive care monitoring and (ii) in which the parameter selection conditions the overall performance. In this sense, we propose an alternative model based on the independent Gaussian properties of the Discrete Fourier Transform (DFT) coefficients, which allows to define a simplified MAP probability function. In addition, the proposed approach defines an adaptive MAP statistical test in which a global hypothesis is defined on particular hypotheses of the multiple observation window. In this sense, the observation interval is modeled as a discontinuous transmission discrete-time stochastic process avoiding the inclusion of parameters that constraint the morphology of the QRS complexes.
| S-EPMC4214710 | biostudies-literature
Project description:MotivationMicroarray experiments can be used to help study the role of chromosomal translocation in cancer development through cancer outlier detection. The aim is to identify genes that are up- or down-regulated in a subset of cancer samples in comparison to normal samples.ResultsWe propose a likelihood-based approach which targets detecting the change of point in mean expression intensity in the group of cancer samples. A desirable property of the proposed approach is the availability of theoretical significance-level results. Simulation studies showed that the performance of the proposed approach is appealing in terms of both detection power and false discovery rate. And the real data example also favored the likelihood-based approach in terms of the biological relevance of the results.AvailabilityR code to implement the proposed method in the statistical package R is available at: http://odin.mdacc.tmc.edu/~jhhu/cod-analysis/.
| S-EPMC2732272 | biostudies-literature
Project description:Recent advancements in smart, wearable technologies have allowed the detection of various medical conditions. In particular, continuous collection and real-time analysis of electrocardiogram data have enabled the early identification of pathologic cardiac rhythms. Various algorithms to assess cardiac rhythms have been developed, but these utilize excessive computational power. Therefore, adoption to mobile platforms requires more computationally efficient algorithms that do not sacrifice correctness. This study presents a modified QRS detection algorithm, the AccYouRate Modified Pan-Tompkins (AMPT), which is a simplified version of the well-established Pan-Tompkins algorithm. Using archived ECG data from a variety of publicly available datasets, relative to the Pan-Tompkins, the AMPT algorithm demonstrated improved computational efficiency by 5-20×, while also universally enhancing correctness, both of which favor translation to a mobile platform for continuous, real-time QRS detection.
| S-EPMC9920820 | biostudies-literature
Project description:We develop a novel nonparametric likelihood ratio test for independence between two random variables using a technique that is free of the common constraints of defining a given set of specific dependence structures. Our methodology revolves around an exact density-based empirical likelihood ratio test statistic that approximates in a distribution-free fashion the corresponding most powerful parametric likelihood ratio test. We demonstrate that the proposed test is very powerful in detecting general structures of dependence between two random variables, including non-linear and/or random-effect dependence structures. An extensive Monte Carlo study confirms that the proposed test is superior to the classical nonparametric procedures across a variety of settings. The real-world applicability of the proposed test is illustrated using data from a study of biomarkers associated with myocardial infarction.
| S-EPMC4191747 | biostudies-literature
Project description:MotivationCancer biology is a field where the complexity of the phenomena battles against the availability of data. Often only a few observations per signal source, i.e. genes, are available. Such scenarios are becoming increasingly more relevant as modern sensing technologies generally have no trouble in measuring lots of channels, but where the number of subjects, such as patients or samples, is limited. In statistics, this problem falls under the heading 'large p, small n'. Moreover, in such situations the use of asymptotic analytical results should generally be mistrusted.ResultsWe consider two cancer datasets, with the aim to mine the activity of functional groups of genes. We propose a hierarchical model with two layers in which the individual signals share a common variance component. A likelihood ratio test is defined for the difference between two collections of corresponding signals. The small number of observations requires a careful consideration of the bias of the statistic, which is corrected through an explicit Bartlett correction. The test is validated on Monte Carlo simulations, which show improved detection of differences compared with other methods. In a leukaemia study and a cancerous fibroblast cell line, we find that the method also works better in practice, i.e. it gives a richer picture of the underlying biology.AvailabilityThe MATLAB code is available from the authors or on http://www.math.rug.nl/stat/Software.Contacte.c.wit@rug.nl d.bakewell@liv.ac.uk.
| S-EPMC3400963 | biostudies-literature
Project description:BackgroundGene duplications are a major source of raw material for evolution and a likely contributor to the diversity of life on earth. Duplicate genes (i.e., homeologs, in the case of a whole genome duplication) may retain their ancestral function, sub- or neofunctionalize, or be lost entirely. A primary way that duplicate genes evolve new functions is by altering their expression patterns. Comparing the expression patterns of duplicate genes gives clues as to whether any of these evolutionary processes have occurred.ResultsWe develop a likelihood ratio test for the analysis of the expression ratios of duplicate genes across two conditions (e.g., tissues). We demonstrate an application of this test by comparing homeolog expression patterns of 1448 homeologous gene pairs using RNA-seq data generated from leaves and petals of an allotetraploid monkeyflower (Mimulus luteus). We assess the sensitivity of this test to different levels of homeolog expression bias and compare the method to several alternatives.ConclusionsThe likelihood ratio test derived here is a direct, transparent, and easily implemented method for detecting changes in homeolog expression bias that outperforms alternative approaches. While our method was derived with homeolog analysis in mind, this method can be used to analyze changes in the ratio of expression levels between any two genes in any two conditions.
| S-EPMC6427896 | biostudies-literature
Project description:Most existing association tests for genome-wide association studies (GWASs) fail to account for genetic heterogeneity. Zhou and Pan proposed a binomial-mixture-model-based association test to account for the possible genetic heterogeneity in case-control studies. The idea is elegant, however, the proposed test requires an expectation-maximization (EM)-type iterative algorithm to identify the penalised maximum likelihood estimates and a permutation method to assess p-values. The intensive computational burden induced by the EM-algorithm and the permutation becomes prohibitive for direct applications to GWASs. This paper develops a likelihood ratio test (LRT) for GWASs under genetic heterogeneity based on a more general alternative mixture model. In particular, a closed-form formula for the LRT statistic is derived to avoid the EM-type iterative numerical evaluation. Moreover, an explicit asymptotic null distribution is also obtained, which avoids using the permutation to obtain p-values. Thus, the proposed LRT is easy to implement for GWASs. Furthermore, numerical studies demonstrate that the LRT has power advantages over the commonly used Armitage trend test and other existing association tests under genetic heterogeneity. A breast cancer GWAS dataset is used to illustrate the newly proposed LRT.
| S-EPMC3910100 | biostudies-literature
Project description:The purpose of this work is to present a computer assisted diagnostic tool for radiologists in their diagnosis of Alzheimer's disease. A statistical likelihood-ratio procedure from signal detection theory was implemented in the detection of Alzheimer's disease. The probability density functions of the likelihood ratio were constructed by using medial temporal lobe (MTL) volumes of patients with Alzheimer's disease (AD) and normal controls (NC). The volumes of MTL as well as other anatomical regions of the brains were calculated by the FreeSurfer software using T1 weighted MRI images. The MRI images of AD and NC were downloaded from the database of Alzheimer's disease neuroimaging initiative (ADNI). A separate dataset of minimal interval resonance imaging in Alzheimer's disease (MIRIAD) was used for diagnostic testing. A sensitivity of 89.1% and specificity of 87.0% were achieved for the MIRIAD dataset which are better than the 85% sensitivity and specificity achieved by the best radiologists without input of other patient information.
| S-EPMC9937475 | biostudies-literature
Project description:The assumption of Hardy-Weinberg equilibrium (HWE) is generally required for association analysis using case-control design on autosomes; otherwise, the size may be inflated. There has been an increasing interest of exploring the association between diseases and markers on X chromosome and the effect of the departure from HWE on association analysis on X chromosome. Note that there are two hypotheses of interest regarding the X chromosome: (i) the frequencies of the same allele at a locus in males and females are equal and (ii) the inbreeding coefficient in females is zero (without excess homozygosity). Thus, excess homozygosity and significantly different minor allele frequencies between males and females are used to filter X-linked variants. There are two existing methods to test for (i) and (ii), respectively. However, their size and powers have not been studied yet. Further, there is no existing method to simultaneously detect both hypotheses till now. Therefore, in this article, we propose a novel likelihood ratio test for both (i) and (ii) on X chromosome. To further investigate the underlying reason why the null hypothesis is statistically rejected, we also develop two likelihood ratio tests for detecting (i) and (ii), respectively. Moreover, we explore the effect of population stratification on the proposed tests. From our simulation study, the size of the test for (i) is close to the nominal significance level. However, the size of the excess homozygosity test and the test for both (i) and (ii) is conservative. So, we propose parametric bootstrap techniques to evaluate their validity and performance. Simulation results show that the proposed methods with bootstrap techniques control the size well under the respective null hypothesis. Power comparison demonstrates that the methods with bootstrap techniques are more powerful than those without bootstrap procedure and the existing methods. The application of the proposed methods to a rheumatoid arthritis dataset indicates their utility.
| S-EPMC4684405 | biostudies-literature
Project description:Testing Hardy-Weinberg equilibrium (HWE) in the control group is commonly used to detect genotyping errors in genetic association studies. We propose a likelihood ratio test for testing HWE in the study population using both case and control samples. This test incorporates underlying association models. Another feature is that, when we infer the disease-genotype association, we explicitly incorporate HWE or a possible departure from Hardy-Weinberg equilibrium (DHWE) into the model. Our unified framework enables us to infer the disease-genotype association when a detected DHWE needs to be part of the model after causes for the DHWE are explored. Real data sets are used to illustrate the application of the methodology and its implication in genetic association studies. Our analysis and interpretation touch on issues such as genotyping errors, population selection, population stratification, or the study sampling plan, that all could be the cause of DHWE.
| S-EPMC2657816 | biostudies-other