Robust algorithm for arrhythmia classification in ECG using extreme learning machine

Background  

Recently, extensive studies have been carried out on arrhythmia classification algorithms using artificial intelligence pattern recognition methods such as neural network. To improve practicality, many studies have focused on learning speed and the accuracy of neural networks. However, algorithms based on neural networks still have some problems concerning practical application, such as slow learning speeds and unstable performance caused by local minima.     

Genetic classification of populations using supervised learning

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case-control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available.In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.     

Gene classification using parameter-free semi-supervised manifold learning

A new manifold learning method, called parameter-free semi-supervised local Fisher discriminant analysis (pSELF), is proposed to map the gene expression data into a low-dimensional space for tumor classification. Motivated by the fact that semi-supervised and parameter-free are two desirable and promising characteristics for dimension reduction, a new difference-based optimization objective function with unlabeled samples has been designed. The proposed method preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution, which can be computed efficiently by eigen decomposition. Experimental results on synthetic data and SRBCT, DLBCL, and Brain Tumor gene expression data sets demonstrate the effectiveness of the proposed method.     

Higher taxonomic descriptors for vegetation classification

A model of direct and diffuse solar radiation on slopes of varying angle and aspect suggests that radiation differences within hilly terrain are maximized in the beginning of the dry season in the monsoon tropics. The differences between north and south facing slopes are greater than those found during the summer in the temperate zone. Within a study area near Mt. Bundey, Northern Territory, floristic and structural vegetational variability is closely related to June radiation as estimated by the model. However, the distribution patterns of monsoon thicket and eucalypt forest relate more to relative five protection than to the effects of incident radiation on temporal patterns of moisture availability. Within both major formations, site rockiness is an important influence on vegetation floristics and structure, both for five protection and through its influence on moisture availability.     

Fall classification by machine learning using mobile phones

Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls-left and right lateral, forward trips, and backward slips-while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls.     

Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization

The study proposes a method for supervised classification of multi-channel surface electromyographic signals with the aim of controlling myoelectric prostheses. The representation space is based on the discrete wavelet transform (DWT) of each recorded EMG signal using unconstrained parameterization of the mother wavelet. The classification is performed with a support vector machine (SVM) approach in a multi-channel representation space. The mother wavelet is optimized with the criterion of minimum classification error, as estimated from the learning signal set. The method was applied to the classification of six hand movements with recording of the surface EMG from eight locations over the forearm. Misclassification rate in six subjects using the eight channels was (mean ± S.D.) 4.7 ± 3.7% with the proposed approach while it was 11.1 ± 10.0% without wavelet optimization (Daubechies wavelet). The DWT and SVM can be implemented with fast algorithms, thus, the method is suitable for real-time implementation.     

Classification of mental tasks from EEG signals using extreme learning machine

In this paper, a recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) is used to classify five mental tasks from different subjects using electroencephalogram (EEG) signals available from a well-known database. Performance of ELM is compared in terms of training time and classification accuracy with a Backpropagation Neural Network (BPNN) classifier and also Support Vector Machines (SVMs). For SVMs, the comparisons have been made for both 1-against-1 and 1-against-all methods. Results show that ELM needs an order of magnitude less training time compared with SVMs and two orders of magnitude less compared with BPNN. The classification accuracy of ELM is similar to that of SVMs and BPNN. The study showed that smoothing of the classifiers' outputs can significantly improve their classification accuracies.     

Paradoxes of Hymenoptera flight muscles,extreme machines

In the Carboniferous, insects evolved flight. Intense selection drove for high performance and approximately 100 million years later, Hymenoptera (bees, wasps and ants) emerged. Some species had proportionately small wings, with apparently impossible aerodynamic challenges including a need for high frequency flight muscles (FMs), powered exclusively off aerobic pathways and resulting in extreme aerobic capacities. Modern insect FMs are the most refined and form large dense blocks that occupy 90% of the thorax. These can beat wings at 200 to 230 Hz, more than double that achieved by standard neuromuscular systems. To do so, rapid repolarisation was circumvented through evolution of asynchronous stimulation, stretch activation, elastic recoil and a paradoxically slow Ca²⁺ reuptake. While the latter conserves ATP, considerable ATP is demanded at the myofibrils. FMs have diminished sarcoplasmic volumes, and ATP is produced solely by mitochondria, which pack myocytes to maximal limits and have very dense cristae. Gaseous oxygen is supplied directly to mitochondria. While FMs appear to be optimised for function, several unusual paradoxes remain. FMs lack any significant equivalent to the creatine kinase shuttle, and myofibrils are twice as wide as those of within cardiomyocytes. The mitochondrial electron transport systems also release large amounts of reactive oxygen species (ROS) and respiratory complexes do not appear to be present at any exceptional level. Given that the loss of the creatine kinase shuttle and elevated ROS impairs heart function, we question how do FM shuttle adenylates at high rates and tolerate oxidative stress conditions that occur in diseased hearts?     

Species classification from hyperspectral leaf information using machine learning approaches

Information on plant species is fundamental to forest ecosystems, in the context of biodiversity monitoring and forest management. Traditional methods for plant species inventories are generally inefficient, in terms of cost and performance, and there is a high demand for a quick and feasible approach to be developed. Of the various attempts, remote sensing has emerged as an active approach for plant species classification, but most studies have concentrated on image processing and only a few of them ever use hyperspectral information, despite the wealth of information it contains. In this study, plant species are classified from hyperspectral leaf information using different machine learning models, coupled with feature reduction and selection methods, and their performance is optimized through Bayesian optimization. The results show that including feature selection and Bayesian optimization increases the classification accuracy of machine learning models. Among these, the Bayesian optimization-based support vector machine (SVM) model, combined with the recursive feature elimination (RFE) feature selection method, yields the best output, with an overall accuracy of 86% and a kappa coefficient of 0.85. Furthermore, the confusion matrix revealed that the number of samples correlates with classification accuracy. The support vector machine with informative bands after Bayesian optimization outperformed in classing plant species. The results of this study facilitate a better understanding of spectral (phenotype) information with plant species (genotype) and help to bridge hyperspectral information with ecosystem functions.     

Practical and configurable network traffic classification using probabilistic machine learning

Cluster Computing - Network traffic classification that is widely applicable and highly accurate is valuable for many network security and management tasks. A flexible and easily configurable...     

Efficient phenotypic sex classification of zebrafish using machine learning methods

Sex determination in zebrafish by manual approaches according to current guidelines relies on human observation. These guidelines for sex recognition have proven to be subjective and highly labor‐intensive. To address this problem, we present a methodology to automatically classify the phenotypic sex using two machine learning methods: Deep Convolutional Neural Networks (DCNNs) based on the whole fish appearance and Support Vector Machine (SVM) based on caudal fin coloration. Machine learning techniques in sex classification provide potential efficiency with the advantage of automatization and robustness in the prediction process. Furthermore, since developmental plasticity can be influenced by environmental conditions, we have investigated the impact of elevated water temperature during embryogenesis on sex and sex‐related differences in color intensity of adult zebrafish. The estimated color intensity based on SVM was then applied to detect the association between coloration and body weight and length. Phenotypic sex classifications using machine learning methods resulted in a high degree of association with the real sex in nontreated animals. In temperature‐induced animals, DCNNs reached a performance of 100%, whereas 20% of males were misclassified using SVM due to a lower color intensity. Furthermore, a positive association between color intensity and body weight and length was observed in males. Our study demonstrates that high ambient temperature leads to a lower color intensity in male animals and a positive association of male caudal fin coloration with body weight and length, which appears to play a significant role in sexual attraction. The software developed for sex classification in this study is readily applicable to other species with sex‐linked visible phenotypic differences.     

Prediction of beta-turns with learning machines

The support vector machine approach was introduced to predict the beta-turns in proteins. The overall self-consistency rate by the re-substitution test for the training or learning dataset reached 100%. Both the training dataset and independent testing dataset were taken from Chou [J. Pept. Res. 49 (1997) 120]. The success prediction rates by the jackknife test for the beta-turn subset of 455 tetrapeptides and non-beta-turn subset of 3807 tetrapeptides in the training dataset were 58.1 and 98.4%, respectively. The success rates with the independent dataset test for the beta-turn subset of 110 tetrapeptides and non-beta-turn subset of 30,231 tetrapeptides were 69.1 and 97.3%, respectively. The results obtained from this study support the conclusion that the residue-coupled effect along a tetrapeptide is important for the formation of a beta-turn.     

Data-dependent kernel machines for microarray data classification

One important application of gene expression analysis is to classify tissue samples according to their gene expression levels. Gene expression data are typically characterized by high dimensionality and small sample size, which makes the classification task quite challenging. In this paper, we present a data-dependent kernel for microarray data classification. This kernel function is engineered so that the class separability of the training data is maximized. A bootstrapping-based resampling scheme is introduced to reduce the possible training bias. The effectiveness of this adaptive kernel for microarray data classification is illustrated with a k-Nearest Neighbor (KNN) classifier. Our experimental study shows that the data-dependent kernel leads to a significant improvement in the accuracy of KNN classifiers. Furthermore, this kernel-based KNN scheme has been demonstrated to be competitive to, if not better than, more sophisticated classifiers such as Support Vector Machines (SVMs) and the Uncorrelated Linear Discriminant Analysis (ULDA) for classifying gene expression data.     

Enzyme family classification by support vector machines

One approach for facilitating protein function prediction is to classify proteins into functional families. Recent studies on the classification of G-protein coupled receptors and other proteins suggest that a statistical learning method, Support vector machines (SVM), may be potentially useful for protein classification into functional families. In this work, SVM is applied and tested on the classification of enzymes into functional families defined by the Enzyme Nomenclature Committee of IUBMB. SVM classification system for each family is trained from representative enzymes of that family and seed proteins of Pfam curated protein families. The classification accuracy for enzymes from 46 families and for non-enzymes is in the range of 50.0% to 95.7% and 79.0% to 100% respectively. The corresponding Matthews correlation coefficient is in the range of 54.1% to 96.1%. Moreover, 80.3% of the 8,291 correctly classified enzymes are uniquely classified into a specific enzyme family by using a scoring function, indicating that SVM may have certain level of unique prediction capability. Testing results also suggest that SVM in some cases is capable of classification of distantly related enzymes and homologous enzymes of different functions. Effort is being made to use a more comprehensive set of enzymes as training sets and to incorporate multi-class SVM classification systems to further enhance the unique prediction accuracy. Our results suggest the potential of SVM for enzyme family classification and for facilitating protein function prediction. Our software is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.     

Automation of species-specific cyanobacteria phycocyanin fluorescence compensation using machine learning classification

High-frequency cyanobacteria monitoring often uses in-situ fluorescence of phycocyanin (f-PC). However, f-PC must be calibrated for the dominant cyanobacteria species, and it cannot distinguish cyanobacteria taxa, which relies on conventional time-consuming cyanobacteria identification methods. This study proposes a framework to automate f-PC species-specific compensation through three components: (1) prediction of the dominant cyanobacteria species using data-driven models and routine environmental monitoring data; (2) determination of species-specific f-PC per biomass in controlled laboratory experiments; and (3) automation of f-PC species compensation. The framework was validated by applying it to Myponga drinking water reservoir in South Australia. Three machine learning techniques using only high-frequency water temperature data were compared to predict the dominant cyanobacteria species. The framework application to Myponga drinking water reservoir improved the agreement of f-PC with conventional cyanobacteria biovolume measurements, and provided rapid, low-cost identification of the dominant cyanobacteria species, which can support proactive species-targeted cyanobacteria management.     

Metagenomic dissection of the canine gut microbiota: insights into taxonomic,metabolic and nutritional features

Domestication of dogs from wolves is the oldest known example of ongoing animal selection, responsible for generating more than 300 dog breeds worldwide. In order to investigate the taxonomic and functional evolution of the canine gut microbiota, a multi-omics approach was applied to six wild wolves and 169 dog faecal samples, the latter encompassing 51 breeds, which fully covers currently known canine genetic biodiversity. Specifically, 16S rRNA gene and bifidobacterial Internally Transcribed Spacer (ITS) profiling were employed to reconstruct and then compare the canine core gut microbiota to those of wolves and humans, revealing that artificial selection and subsequent cohabitation of dogs with their owners influenced the microbial population of canine gut through loss and acquisition of specific bacterial taxa. Moreover, comparative analysis of the intestinal bacterial population of dogs fed on Bones and Raw Food (BARF) or commercial food (CF) diet, coupled with shotgun metagenomics, highlighted that both bacterial composition and metabolic repertoire of the canine gut microbiota have evolved to adapt to high-protein or high-carbohydrates intake. Altogether, these data indicate that artificial selection and domestication not only affected the canine genome, but also shaped extensively the bacterial population harboured by the canine gut.     

Metagenomic analysis of taxonomic and functional changes in gut microbiota of patients with the alcohol dependence syndrome

The first metagenomic study of gut microbiota in patients with the alcohol dependence syndrome (ADS) has been performed in the whole-genome sequencing (“shotgun”) format. Taxonomic analysis revealed changes in the relative abundance of the predominant bacteria associated with inflammatioln (including increased levels of Ruminococcus gnavus and R. torques, and decreased levels of Faecalibacterium and Akkermansia genera). The microbiota of ADS patients was characterized by the presence of opportunistic pathogens rarely detected in metagenomes of healthy individuals from different countries. Comparative analysis of total metabolic potential revealed increased relative abundance of KEGG pathways associated with the response to oxidative stress. ADS patients also had increased levels of two specific groups of genes encoding enzymes involved in the metabolism of alcohol, as well as virulence factors. It is possible that gut microbiota of ADS patients demonstrating changes in both taxonomic and functional composition plays a role in modulating the effects of alcohol on the host body     

Rapid quantification and taxonomic classification of environmental DNA from both prokaryotic and eukaryotic origins using a microarray

A microarray has been designed using 62,358 probes matched to both prokaryotic and eukaryotic small-subunit ribosomal RNA genes. The array categorized environmental DNA to specific phylogenetic clusters in under 9 h. To a background of DNA generated from natural outdoor aerosols, known quantities of rRNA gene copies from distinct organisms were added producing corresponding hybridization intensity scores that correlated well with their concentrations (r=0.917). Reproducible differences in microbial community composition were observed by altering the genomic DNA extraction method. Notably, gentle extractions produced peak intensities for Mycoplasmatales and Burkholderiales, whereas a vigorous disruption produced peak intensities for Vibrionales, Clostridiales, and Bacillales.