Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76

Ectomycorrhizal fungal communities of native and non-native Pinus and Quercus species in a common garden of 35-year-old trees

Non-native tree species have been widely planted or have become naturalized in most forested landscapes. It is not clear if native trees species collectively differ in ectomycorrhizal fungal (EMF) diversity and communities from that of non-native tree species. Alternatively, EMF species community similarity may be more determined by host plant phylogeny than by whether the plant is native or non-native. We examined these unknowns by comparing two genera, native and non-native Quercus robur and Quercus rubra and native and non-native Pinus sylvestris and Pinus nigra in a 35-year-old common garden in Poland. Using molecular and morphological approaches, we identified EMF species from ectomycorrhizal root tips and sporocarps collected in the monoculture tree plots. A total of 69 EMF species were found, with 38 species collected only as sporocarps, 18 only as ectomycorrhizas, and 13 both as ectomycorrhizas and sporocarps. The EMF species observed were all native and commonly associated with a Holarctic range in distribution. We found that native Q. robur had ca. 120% higher total EMF species richness than the non-native Q. rubra, while native P. sylvestris had ca. 25% lower total EMF species richness than non-native P. nigra. Thus, across genera, there was no evidence that native species have higher EMF species diversity than exotic species. In addition, we found a higher similarity in EMF communities between the two Pinus species than between the two Quercus species. These results support the naturalization of non-native trees by means of mutualistic associations with cosmopolitan and novel fungi

OWA-based robust fuzzy clustering of time series with typicality degrees

In many cases, data are not expressed as individual values on a timeline, but are a collection of values obtained at certain moments in time - they are time series. In these cases, traditional clustering models for one-time data are unable to properly account for the time-variability of the data. In this paper, by considering the partitioning around medoids approach in a fuzzy framework, we propose fuzzy clustering models for multivariate time series. In order to neutralize the negative effects of outlier time series in the clustering process, we proposed robust fuzzy c-medoids clustering models for time series based on the combination of Huber's M-estimators and Yager's OWA operators. The proposed models are able to smooth the influence of anomalous time series by means of the so-called typicality parameter, capable to tune the influence of the outliers. The performance of the proposed models has been shown by means of a simulation and real-data sets study: (i) two-dimensional dataset of time series, (ii) the average daily time series of temperatures, and (iii) the pregnancy dataset of time series. The comparison made with the robust clustering models known from the literature indicates the competitiveness of the introduced model to others

Linguistically Defined Clustering of Data

This paper introduces a method of data clustering that is based on linguistically specified rules, similar to those applied by a human visually fulfilling a task. The method endeavors to follow these remarkable capabilities of intelligent beings. Even for most complicated data patterns a human is capable of accomplishing the clustering process using relatively simple rules. His/her way of clustering is a sequential search for new structures in the data and new prototypes with the use of the following linguistic rule: search for prototypes in regions of extremely high data densities and immensely far from the previously found ones. Then, after this search has been completed, the respective data have to be assigned to any of the clusters whose nuclei (prototypes) have been found. A human again uses a simple linguistic rule: data from regions with similar densities, which are located exceedingly close to each other, should belong to the same cluster. The goal of this work is to prove experimentally that such simple linguistic rules can result in a clustering method that is competitive with the most effective methods known from the literature on the subject. A linguistic formulation of a validity index for determination of the number of clusters is also presented. Finally, an extensive experimental analysis of benchmark datasets is performed to demonstrate the validity of the clustering approach introduced. Its competitiveness with the state-of-the-art solutions is also shown