Electronic Fetal Monitoring in the form of cardiotocography is routinely used for fetal assessment both during pregnancy and delivery. However its interpretation requires a high level of expertise and even then the assessment is somewhat subjective as it has been proven by the high inter and intra-observer variability. Therefore the scientific community seeks for more objective methods for its interpretation. Along this path, presented work proposes a classification approach, which is based on a latent class analysis method that attempts to produce more objective labeling of the training cases, a step which is vital in a classification problem. The method is combined with a simple logistic regression approach under two different schemes: a standard multi-class classification formulation and an ordinal classification one. The results are promising suggesting that more effort should be put in this proposed approach.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1109/EMBC.2014.6943525 | DOI Listing |
Netw Neurosci
December 2024
Institute of Neurosciences and Medicine - Brain and Behaviour (INM-7), Research Centre Jülich, 52425 Jülich, Germany.
The neurodegenerative progression of Parkinson's disease affects brain structure and function and, concomitantly, alters the topological properties of brain networks. The network alteration accompanied by motor impairment and the duration of the disease has not yet been clearly demonstrated in the disease progression. In this study, we aim to resolve this problem with a modeling approach using the reduced Jansen-Rit model applied to large-scale brain networks derived from cross-sectional MRI data.
View Article and Find Full Text PDFJ Med Imaging (Bellingham)
December 2024
University of Houston, Department of Physics, Houston, Texas, United States.
Purpose: Photon counting detectors offer promising advancements in computed tomography (CT) imaging by enabling the quantification and three-dimensional imaging of contrast agents and tissue types through simultaneous multi-energy projections from broad X-ray spectra. However, the accuracy of these decomposition methods hinges on precise composite spectral attenuation values that one must reconstruct from spectral micro-CT. Errors in such estimations could be due to effects such as beam hardening, object scatter, or detector sensor-related spectral distortions such as fluorescence.
View Article and Find Full Text PDFTurk J Med Sci
December 2024
Cerebral Palsy and Pediatric Rehabilitation Unit, Faculty of Physical Therapy and Rehabilitation, Hacettepe University, Ankara, Turkiye.
Background/aim: Functional asymmetry in the upper extremities may occur in infants with neuromotor problems due to neurodevelopmental or musculoskeletal disorders. The aim of this study was to investigate the validity and reliability of the Turkish version of the Infant Motor Activity Log (IMAL-T), which assesses the frequency (how often) and quality (how well) of the affected arm usage during activities in infants with functional asymmetry in the upper extremities.
Materials And Methods: The IMAL-T was administered face-to-face to the parents of 102 infants [60 infants at high risk of developing cerebral palsy (CP) and 42 infants with brachial plexus birth injury (BPBI)], aged 6-24 months, with functional asymmetry in the upper extremities.
J Eval Clin Pract
February 2025
Instituto Mexicano del Seguro Social, IMSS Hospital General de Zona Número 17, Monterrey, Nuevo León, México.
Introduction: Rheumatoid arthritis (RA) is a progressive autoimmune inflammatory disease. According to the European League Against Rheumatism (EULAR), the stages of RA progression include pre-RA, preclinical RA, inflammatory arthralgia, arthralgia with positive antibodies, arthralgia suspected of progressing to RA, undifferentiated arthritis and finally established RA. According to the Community Oriented Program for Control of Rheumatic Diseases (COPCORD), the prevalence of RA in Mexico is 1.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Medical Sciences, University of Torino, Torino, Italy.
Classification and regression problems can be challenging when the relevant input features are diluted in noisy datasets, in particular when the sample size is limited. Traditional Feature Selection (FS) methods address this issue by relying on some assumptions such as the linear or additive relationship between features. Recently, a proliferation of Deep Learning (DL) models has emerged to tackle both FS and prediction at the same time, allowing non-linear modeling of the selected features.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!