A novel adaptive algorithm for the blind separation of periodic sources

An adaptive algorithm for the blind separation of periodic sources is proposed in this paper. The method uses only the second order statistics of the data, and exploits the periodic nature of the source signals. Simulation results show that the proposed approach has the ability to restore statistical independence, and its performance is comparable to that of a well established, higher order, blind source separation method

Normalised natural gradient algorithm for the separation of cyclostationary sources

A normalised natural gradient algorithm (NGA) for the separation of cyclostationary source signals is proposed in this paper. It improves the convergence properties of the cyclostationary natural gradient algorithm (CSNGA) by employing a gradient adaptive learning rate whose value changes in response to some change in the filter parameters. Experimental results demonstrate the improved behaviour of the approach

Fetal electrocardiogram extraction by sequential source separation in the wavelet domain

This work addresses the problem of fetal electrocardiogram extraction using blind source separation (BSS) in the wavelet domain. A new approach is proposed, which is particularly advantageous when the mixing environment is noisy and time-varying, and that is shown, analytically and in simulation, to improve the convergence rate of the natural gradient algorithm. The distribution of the wavelet coefficients of the source signals is then modeled by a generalized Gaussian probability density, whereby in the time-scale domain the problem of selecting appropriate nonlinearities when separating mixtures of both sub- and super-Gaussian signals is mitigated, as shown by experimental results

Blind separation of convolutive mixtures of cyclostationary sources using an extended natural gradient method

An on-line adaptive blind source separation algorithm for the separation of convolutive mixtures of cyclostationary source signals is proposed. The algorithm is derived by applying natural gradient iterative learning to the novel cost function which is defined according to the wide sense cyclostationarity of signals. The efficiency of the algorithm is supported by simulations, which show that the proposed algorithm has improved performance for the separation of convolved cyclostationary signals in terms of convergence speed and waveform similarity measurement, as compared to the conventional natural gradient algorithm for convolutive mixtures

An adaptive stereo basis method for convolutive blind audio source separation

NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in PUBLICATION, [71, 10-12, June 2008] DOI:neucom.2007.08.02

Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F1-score of 0.65, performing on par with the inter-observer agreement. The dataset and developed methods can facilitate further biomedical research on automated interpretation of lung ultrasound with the potential to expand the clinical utility.Comment: 10 pages, 4 figure

The Impact of HIV Infection and CD4 Cell Count on the Performance of an Interferon Gamma Release Assay in Patients with Pulmonary Tuberculosis

BACKGROUND:The performance of the tuberculosis specific Interferon Gamma Release Assays (IGRAs) has not been sufficiently documented in tuberculosis- and HIV-endemic settings. This study evaluated the sensitivity of the QuantiFERON TB-Gold In-Tube (QFT-IT) in patients with culture confirmed pulmonary tuberculosis (PTB) in a TB- and HIV-endemic population and the effect of HIV-infection and CD4 cell count on test performance. METHODOLOGY/PRINCIPAL FINDINGS:161 patients with sputum culture confirmed PTB were subjected to HIV- and QFT-IT testing and measurement of CD4 cell count. The QFT-IT was positive in 74% (119/161; 95% CI: 67-81%). Sensitivity was higher in HIV-negative (75/93) than in HIV-positive (44/68) patients (81% vs. 65%, p = 0.02) and increased with CD4 cell count in HIV-positive patients (test for trend p = 0.03). 23 patients (14%) had an indeterminate result and this proportion decreased with increasing CD4 cell count in HIV-positive patients (test for trend p = 0.03). Low CD4 cell count (<300 cells/microl) did not account for all QFT-IT indeterminate nor all negative results. Sensitivity when excluding indeterminate results was 86% (95% CI: 81-92%) and did not differ between HIV-negative and HIV-positive patients (88 vs. 83%, p = 0.39). CONCLUSIONS/SIGNIFICANCE:Sensitivity of the QFT-IT for diagnosing active PTB infection was reasonable when excluding indeterminate results and in HIV-negative patients. However, since the test missed more than 10% of patients, its potential as a rule-out test for active TB disease is limited. Furthermore, test performance is impaired by low CD4 cell count in HIV-positive patients and possibly by other factors as well in both HIV-positive and HIV-negative patients. This might limit the potential of the test in populations where HIV-infection is prevalent