102 research outputs found
Measuring the Cosmic Ray Muon-Induced Fast Neutron Spectrum by (n,p) Isotope Production Reactions in Underground Detectors
While cosmic ray muons themselves are relatively easy to veto in underground
detectors, their interactions with nuclei create more insidious backgrounds
via: (i) the decays of long-lived isotopes produced by muon-induced spallation
reactions inside the detector, (ii) spallation reactions initiated by fast
muon-induced neutrons entering from outside the detector, and (iii) nuclear
recoils initiated by fast muon-induced neutrons entering from outside the
detector. These backgrounds, which are difficult to veto or shield against, are
very important for solar, reactor, dark matter, and other underground
experiments, especially as increased sensitivity is pursued. We used fluka to
calculate the production rates and spectra of all prominent secondaries
produced by cosmic ray muons, in particular focusing on secondary neutrons, due
to their importance. Since the neutron spectrum is steeply falling, the total
neutron production rate is sensitive just to the relatively soft neutrons, and
not to the fast-neutron component. We show that the neutron spectrum in the
range between 10 and 100 MeV can instead be probed by the (n, p)-induced
isotope production rates 12C(n, p)12B and 16O(n, p)16N in oil- and water-based
detectors. The result for 12B is in good agreement with the recent KamLAND
measurement. Besides testing the calculation of muon secondaries, these results
are also of practical importance, since 12B (T1/2 = 20.2 ms, Q = 13.4 MeV) and
16N (T1/2 = 7.13 s, Q = 10.4 MeV) are among the dominant spallation backgrounds
in these detectors
A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure-free electroencephalography signals
Properly determining the discriminative features which characterize the inherent behaviors of electroencephalography (EEG) signals remains a great challenge for epileptic seizure detection. In this present study, a novel feature selection scheme based on the discrete wavelet packet decomposition and cuckoo search algorithm (CSA) was proposed. The normal as well as epileptic EEG recordings were frst decomposed into various frequency bands by means of wavelet packet decomposition, and subsequently, statistical features at all developed nodes in the wavelet packet decomposition tree were derived. Instead of using the complete set of the extracted features to construct a wavelet neural networks-based classifer, an optimal feature subset that maximizes the predictive competence of the classifer was selected by using the CSA. Experimental results on the publicly available benchmarks demonstrated that the proposed feature subset selection scheme achieved promising recognition accuracies of 98.43â100%, and the results were statistically signifcant using z-test with p value <0.0001
Scaling Effects and Spatio-Temporal Multilevel Dynamics in Epileptic Seizures
Epileptic seizures are one of the most well-known dysfunctions of the nervous system. During a seizure, a highly synchronized behavior of neural activity is observed that can cause symptoms ranging from mild sensual malfunctions to the complete loss of body control. In this paper, we aim to contribute towards a better understanding of the dynamical systems phenomena that cause seizures. Based on data analysis and modelling, seizure dynamics can be identified to possess multiple spatial scales and on each spatial scale also multiple time scales. At each scale, we reach several novel insights. On the smallest spatial scale we consider single model neurons and investigate early-warning signs of spiking. This introduces the theory of critical transitions to excitable systems. For clusters of neurons (or neuronal regions) we use patient data and find oscillatory behavior and new scaling laws near the seizure onset. These scalings lead to substantiate the conjecture obtained from mean-field models that a Hopf bifurcation could be involved near seizure onset. On the largest spatial scale we introduce a measure based on phase-locking intervals and wavelets into seizure modelling. It is used to resolve synchronization between different regions in the brain and identifies time-shifted scaling laws at different wavelet scales. We also compare our wavelet-based multiscale approach with maximum linear cross-correlation and mean-phase coherence measures
The Electroencephalogram as a Biomarker Based on Signal Processing Using Nonlinear Techniques to Detect Dementia
Dementia being a syndrome caused by a brain disease of a chronic or
progressive nature, in which the irreversible loss of intellectual abilities, learning, expressions arises; including memory, thinking, orientation, understanding
and adequate communication, of organizing daily life and of leading a family,
work and autonomous social life; leads to a state of total dependence; therefore,
its early detection and classification is of vital importance in order to serve as
clinical support for physicians in the personalization of treatment programs. The
use of the electroencephalogram as a tool for obtaining information on the
detection of changes in brain activities. This article reviews the types of cognitive spectrum dementia, biomarkers for the detection of dementia, analysis of
mental states based on electromagnetic oscillations, signal processing given by
the electroencephalogram, review of processing techniques, results obtained
where it is proposed the mathematical model about neural networks, discussion
and finally the conclusions
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