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Register a new userThe CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification
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Cardiac auscultation is one of the most cost-effective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on auscultation can support physicians in their decisions. Unfortunately, the application of such systems in clinical trials is still minimal since most of them only aim to detect the presence of extra or abnormal waves in the phonocardiogram signal. This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e.g., cardiac murmurs) exists. As a result, current machine learning algorithms are unable to classify such waves. To pave the way to more effective research on healthcare recommendation systems based on auscultation, our team has prepared the currently largest pediatric heart sound dataset. A total of 5282 recordings have been collected from the four main auscultation locations of 1568 patients, in the process 215780 heart sounds have been manually annotated. Furthermore, and for the first time, each cardiac murmur has been manually annotated by an expert annotator according to its timing, shape, pitch, grading and quality. In addition, the auscultation locations where the murmur is present were identified as well as the auscultation location where the murmur is detected more intensively.
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MURMUR is a new passing-through-walls neutron experiment designed to constrain neutron/hidden neutron transitions allowed in the context of braneworld scenarios or mirror matter models. A nuclear reactor can act as a hidden neutron source, such that neutrons travel through a hidden world or sector. Hidden neutrons can propagate out of the nuclear core and far beyond the biological shielding. However, hidden neutrons can weakly interact with usual matter, making possible for their detection in the context of low-noise measurements. In the present work, the novelty rests on a better background discrimination and the use of a mass of a material - here lead - able to enhance regeneration of hidden neutrons into visible ones to improve detection. The input of this new setup is studied using both modelizations and experiments, thanks to tests currently performed with the experiment at the BR2 research nuclear reactor (SCK$cdot$CEN, Mol, Belgium). A new limit on the neutron swapping probability p has been derived thanks to the measurements taken during the BR2 Cycle 02/2019A: $p < 4.0 times 10^{-10}$ at 95% CL. This constraint is better than the bound from the previous passing-through-wall neutron experiment made at ILL in 2015, despite BR2 is less efficient to generate hidden neutrons by a factor 7.4, thus raising the interest of such experiment using regenerating materials.
Specified Certainty Classification (SCC) is a new paradigm for employing classifiers whose outputs carry uncertainties, typically in the form of Bayesian posterior probabilities. By allowing the classifier output to be less precise than one of a set of atomic decisions, SCC allows all decisions to achieve a specified level of certainty, as well as provides insights into classifier behavior by examining all decisions that are possible. Our primary illustration is read classification for reference-guided genome assembly, but we demonstrate the breadth of SCC by also analyzing COVID-19 vaccination data.
Chronic Kidney Disease (CKD) is an increasingly prevalent condition affecting 13% of the US population. The disease is often a silent condition, making its diagnosis challenging. Identifying CKD stages from standard office visit records can help in early detection of the disease and lead to timely intervention. The dataset we use is highly imbalanced. We propose a hierarchical meta-classification method, aiming to stratify CKD by severity levels, employing simple quantitative non-text features gathered from office visit records, while addressing data imbalance. Our method effectively stratifies CKD severity levels obtaining high average sensitivity, precision and F-measure (~93%). We also conduct experiments in which the dimensionality of the data is significantly reduced to include only the most salient features. Our results show that the good performance of our system is retained even when using the reduced feature sets, as well as under much reduced training sets, indicating that our method is stable and generalizable.
The Andromeda galaxy (M31) hosts a central super-massive black hole (SMBH), known as M31$^ast$, which is remarkable for its mass ($sim$$10^8{rm~M_odot}$) and extreme radiative quiescence. Over the past decade, the Chandra X-ray observatory has pointed to the center of M31 $sim$100 times and accumulated a total exposure of $sim$900 ks. Based on these observations, we present an X-ray study of a highly variable source that we associate with M31$^ast$ based on positional coincidence. We find that M31$^ast$ remained in a quiescent state from late 1999 to 2005, exhibiting an average 0.5-8 keV luminosity $lesssim$$10^{36}{rm~ergs~s^{-1}}$, or only $sim$$10^{-10}$ of its Eddington luminosity. We report the discovery of an outburst that occurred on January 6, 2006, during which M31$^ast$ radiated at $sim$$4.3times10^{37}{rm~ergs~s^{-1}}$. After the outburst, M31$^ast$ entered a more active state that apparently lasts to the present, which is characterized by frequent flux variability around an average luminosity of $sim$$4.8times10^{36}{rm~ergs~s^{-1}}$. These flux variations are similar to the X-ray flares found in the SMBH of our Galaxy (Sgr A$^ast$), making M31$^ast$ the second SMBH known to exhibit recurrent flares. Future coordinated X-ray/radio observations will provide useful constraints on the physical origin of the flaring emission and help rule out a possible stellar origin of the X-ray source.
Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are less accurate across the variety of impacts that patients may undergo. We investigated the spectral characteristics of different head impact types with kinematics classification. Data was analyzed from 3,262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football), reaching a median accuracy of 96% over 1,000 random partitions of training and test sets. To test the classifier on data from different measurement devices, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards with the classifier reaching over 96% accuracy. The most important features in the classification included both low-frequency and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low-frequency and high-frequency ranges (e.g., the spectral densities of MMA impacts were higher in high-frequency range than in the low-frequency range). Finally, with the classifier, type-specific, nearest-neighbor regression models were built for 95th percentile maximum principal strain, 95th percentile maximum principal strain in corpus callosum, and cumulative strain damage (15th percentile). This showed a generally higher R2-value than baseline models. The classifier enables a better understanding of the impact kinematics in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation. Key words: traumatic brain injury, head impacts, classification, impact kinematics
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