73,357 research outputs found
Extracting fetal heart beats from maternal abdominal recordings: Selection of the optimal principal components
This study presents a systematic comparison of different approaches to the automated selection of the principal components (PC) which optimise the detection of maternal and fetal heart beats from non-invasive maternal abdominal recordings. A public database of 75 4-channel non-invasive maternal abdominal recordings was used for training the algorithm. Four methods were developed and assessed to determine the optimal PC: (1) power spectral distribution, (2) root mean square, (3) sample entropy, and (4) QRS template. The sensitivity of the performance of the algorithm to large-amplitude noise removal (by wavelet de-noising) and maternal beat cancellation methods were also assessed. The accuracy of maternal and fetal beat detection was assessed against reference annotations and quantified using the detection accuracy score F1 [2*PPV*Se / (PPV + Se)], sensitivity (Se), and positive predictive value (PPV). The best performing implementation was assessed on a test dataset of 100 recordings and the agreement between the computed and the reference fetal heart rate (fHR) and fetal RR (fRR) time series quantified. The best performance for detecting maternal beats (F1 99.3%, Se 99.0%, PPV 99.7%) was obtained when using the QRS template method to select the optimal maternal PC and applying wavelet de-noising. The best performance for detecting fetal beats (F1 89.8%, Se 89.3%, PPV 90.5%) was obtained when the optimal fetal PC was selected using the sample entropy method and utilising a fixed-length time window for the cancellation of the maternal beats. The performance on the test dataset was 142.7 beats2/min2 for fHR and 19.9 ms for fRR, ranking respectively 14 and 17 (out of 29) when compared to the other algorithms presented at the Physionet Challenge 2013
Metastable Evolutionary Dynamics: Crossing Fitness Barriers or Escaping via Neutral Paths?
We analytically study the dynamics of evolving populations that exhibit
metastability on the level of phenotype or fitness. In constant selective
environments, such metastable behavior is caused by two qualitatively different
mechanisms. One the one hand, populations may become pinned at a local fitness
optimum, being separated from higher-fitness genotypes by a {\em fitness
barrier} of low-fitness genotypes. On the other hand, the population may only
be metastable on the level of phenotype or fitness while, at the same time,
diffusing over {\em neutral networks} of selectively neutral genotypes.
Metastability occurs in this case because the population is separated from
higher-fitness genotypes by an {\em entropy barrier}: The population must
explore large portions of these neutral networks before it discovers a rare
connection to fitter phenotypes.
We derive analytical expressions for the barrier crossing times in both the
fitness barrier and entropy barrier regime. In contrast with ``landscape''
evolutionary models, we show that the waiting times to reach higher fitness
depend strongly on the width of a fitness barrier and much less on its height.
The analysis further shows that crossing entropy barriers is faster by orders
of magnitude than fitness barrier crossing. Thus, when populations are trapped
in a metastable phenotypic state, they are most likely to escape by crossing an
entropy barrier, along a neutral path in genotype space. If no such escape
route along a neutral path exists, a population is most likely to cross a
fitness barrier where the barrier is {\em narrowest}, rather than where the
barrier is shallowest.Comment: 32 pages, 7 figures, 1 table;
http://www.santafe.edu/projects/evca/med.ps.g
Exploring EEG Features in Cross-Subject Emotion Recognition
Recognizing cross-subject emotions based on brain imaging data, e.g., EEG, has always been difficult due to the poor generalizability of features across subjects. Thus, systematically exploring the ability of different EEG features to identify emotional information across subjects is crucial. Prior related work has explored this question based only on one or two kinds of features, and different findings and conclusions have been presented. In this work, we aim at a more comprehensive investigation on this question with a wider range of feature types, including 18 kinds of linear and non-linear EEG features. The effectiveness of these features was examined on two publicly accessible datasets, namely, the dataset for emotion analysis using physiological signals (DEAP) and the SJTU emotion EEG dataset (SEED). We adopted the support vector machine (SVM) approach and the "leave-one-subject-out" verification strategy to evaluate recognition performance. Using automatic feature selection methods, the highest mean recognition accuracy of 59.06% (AUC = 0.605) on the DEAP dataset and of 83.33% (AUC = 0.904) on the SEED dataset were reached. Furthermore, using manually operated feature selection on the SEED dataset, we explored the importance of different EEG features in cross-subject emotion recognition from multiple perspectives, including different channels, brain regions, rhythms, and feature types. For example, we found that the Hjorth parameter of mobility in the beta rhythm achieved the best mean recognition accuracy compared to the other features. Through a pilot correlation analysis, we further examined the highly correlated features, for a better understanding of the implications hidden in those features that allow for differentiating cross-subject emotions. Various remarkable observations have been made. The results of this paper validate the possibility of exploring robust EEG features in cross-subject emotion recognition
A Generic Security Proof for Quantum Key Distribution
Quantum key distribution allows two parties, traditionally known as Alice and
Bob, to establish a secure random cryptographic key if, firstly, they have
access to a quantum communication channel, and secondly, they can exchange
classical public messages which can be monitored but not altered by an
eavesdropper, Eve. Quantum key distribution provides perfect security because,
unlike its classical counterpart, it relies on the laws of physics rather than
on ensuring that successful eavesdropping would require excessive computational
effort. However, security proofs of quantum key distribution are not trivial
and are usually restricted in their applicability to specific protocols. In
contrast, we present a general and conceptually simple proof which can be
applied to a number of different protocols. It relies on the fact that a
cryptographic procedure called privacy amplification is equally secure when an
adversary's memory for data storage is quantum rather than classical.Comment: Analysis of B92 protocol adde
On the entropy of protein families
Proteins are essential components of living systems, capable of performing a
huge variety of tasks at the molecular level, such as recognition, signalling,
copy, transport, ... The protein sequences realizing a given function may
largely vary across organisms, giving rise to a protein family. Here, we
estimate the entropy of those families based on different approaches, including
Hidden Markov Models used for protein databases and inferred statistical models
reproducing the low-order (1-and 2-point) statistics of multi-sequence
alignments. We also compute the entropic cost, that is, the loss in entropy
resulting from a constraint acting on the protein, such as the fixation of one
particular amino-acid on a specific site, and relate this notion to the escape
probability of the HIV virus. The case of lattice proteins, for which the
entropy can be computed exactly, allows us to provide another illustration of
the concept of cost, due to the competition of different folds. The relevance
of the entropy in relation to directed evolution experiments is stressed.Comment: to appear in Journal of Statistical Physic
Discriminating different classes of biological networks by analyzing the graphs spectra distribution
The brain's structural and functional systems, protein-protein interaction,
and gene networks are examples of biological systems that share some features
of complex networks, such as highly connected nodes, modularity, and
small-world topology. Recent studies indicate that some pathologies present
topological network alterations relative to norms seen in the general
population. Therefore, methods to discriminate the processes that generate the
different classes of networks (e.g., normal and disease) might be crucial for
the diagnosis, prognosis, and treatment of the disease. It is known that
several topological properties of a network (graph) can be described by the
distribution of the spectrum of its adjacency matrix. Moreover, large networks
generated by the same random process have the same spectrum distribution,
allowing us to use it as a "fingerprint". Based on this relationship, we
introduce and propose the entropy of a graph spectrum to measure the
"uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon
divergences between graph spectra to compare networks. We also introduce
general methods for model selection and network model parameter estimation, as
well as a statistical procedure to test the nullity of divergence between two
classes of complex networks. Finally, we demonstrate the usefulness of the
proposed methods by applying them on (1) protein-protein interaction networks
of different species and (2) on networks derived from children diagnosed with
Attention Deficit Hyperactivity Disorder (ADHD) and typically developing
children. We conclude that scale-free networks best describe all the
protein-protein interactions. Also, we show that our proposed measures
succeeded in the identification of topological changes in the network while
other commonly used measures (number of edges, clustering coefficient, average
path length) failed
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