Progress of classification algorithms for motor imagery electroencephalogram signals / 生物医学工程学杂志

Motor imagery (MI), motion intention of the specific body without actual movements, has attracted wide attention in fields as neuroscience. Classification algorithms for motor imagery electroencephalogram (MI-EEG) signals are able to distinguish different MI tasks based on the physiological information contained by the EEG signals, especially the features extracted from them. In recent years, there have been some new advances in classification algorithms for MI-EEG signals in terms of classifiers versus machine learning strategies. In terms of classifiers, traditional machine learning classifiers have been improved by some researchers, deep learning and Riemannian geometry classifiers have been widely applied as well. In terms of machine learning strategies, ensemble learning, adaptive learning, and transfer learning strategies have been utilized to improve classification accuracies or reach other targets. This paper reviewed the progress of classification algorithms for MI-EEG signals, summarized and evaluated the existing classifiers and machine learning strategies, to provide new ideas for developing classification algorithms with higher performance.

Classification of Control and Neurodegenerative Disease Subjects Using Tree Based Classifiers

Background: The medical researchers are developing different non-invasive methods for early detection of Neurodegenerative Diseases (NDDs) when pharmacological interventions are still possible to further prevent the disease progression. The NDDs are associated with the degradation in the complex gait dynamicsand motor activity. The classification ofgait data using machine learning techniques can assist the physiciansfor early diagnosis of the neural disorder when clinical manifestation of the diseases is not yet apparent. Aims: The present study was undertaken to classify the control and NDD subjects using decision trees based classifiers (Random Forest (RF), J48 and REPTree).Methodology:The data used in the study comprises of 16 control, 20 Huntington’s Disease (HD), 15 Parkinson’s Disease (PD), and 13 Amyotrophic Lateral Sclerosis (ALS) subjects, which were taken from publicly available database from Physionet. The age range of control subjects was 20-74, HD subjects was 36-70, PD subjects was 44-80, and ALS subjects was 29-71. There were 13 attributes associated with the data. Important features/attributes of the data were selected using correlation feature selection -subset evaluation (cfs) method. Three tree based machine learning algorithms (RF, J48 and REPTree) were used to classify the control and NDD subjects. The performance of classifiers were evaluated using Precision, Recall, F-Measure, MAE and RMSE.Results:In order to evaluate the performance of tree based classifiers, two different settings of data i.e. complete features and selected featureswere used. In classifying control vs HD subjects, RF provides the robust separation with classification accuracy of 84.79% using complete features and 83.94% using selected features. While in classifying control vs PD subjects, and control vs ALS subjects, RF also provides the best separation with classification accuracy of 86.51% and 94.95% respectively using complete features and 85.19% and 93.64% respectively using selected features.Conclusion:The variability analysis of physiological signals provides a valuable non-invasive tool for quantifying the system of dynamics of healthy subjects and to examine the alternations in the controlling mechanism of these systems with aging and disease. It is concluded that selected features encode adequate information about neural control of the gait. Moreover,the selected featuresalong with tree based machine learning algorithms can play a vital for early detection of NDDs, when pharmacological interventions are still possible

Diabetes Prediction Using Ensemble Classifier

Diabetes is one of the impactful diseases that affect humans’ health rigorously. Early diagnosis of diabetes will assist health caresystems to decide and act according to counter measures. This paper focuses on obtaining an automated tool that will predictdiabetic tendency of a patient. The system proposed by this paper contains two ensemble classifiers- Voting ensemble classifierand Stacking Ensemble classifier. Both of these methods exhibits better results while compared to other classifiers. Stackingensemble classifier even performs better than voting ensemble classifier with an accuracy of 79.87%.

A Novel Run-length based wavelet features for Screening Thyroid Nodule Malignancy

Abstract: Thyroid nodules are cell growths in the thyroid which might be for in one of two categories benign or malignant. Nodular thyroid disease is common and because of the associated risk of malignancy and hyper-function; these nodules have to be examined thoroughly. Hence diagnosing thyroid nodule malignancy in the early stage can mitigate the possibility of death. This paper presents an intelligent thyroid nodules malignancy diagnosis using texture information in run-length matrix derived from 2- level 2D wavelet transform bands (approximation and details). In this work, ANOVA test has been used to for feature selection to reduce for feature selection about 45 run-length features with and without wavelet generated, before feeding those features which clinical importance to the Support Vector Machine(SVM) and Decision Tree (DT) classifier to perform the automated diagnosis. The validation of this work is activated using 100-thyroid nodule images spliced equally between the two categories (50 Benign and 50 Malignant). The proposed system can detect thyroid nodules malignancy with an average accuracy of about 97% using SVM classifier for the run- length matrix, features derived from spatial domain while the average accuracy is increased to 98% in case of hybrid feature derived from spatial domain and 2-level wavelet decomposition. For the other proposed classifier (DT), the average accuracy in case of spatial domain based features is 93% whereas the average accuracy of the hybrid features system is 97%. Hence the proposed system can be used for the screening of thyroid nodules.

Control of intelligent car based on electroencephalogram and neurofeedback / 生物医学工程学杂志

To improve the performance of brain-controlled intelligent car based on motor imagery (MI), a method based on neurofeedback (NF) with electroencephalogram (EEG) for controlling intelligent car is proposed. A mental strategy of MI in which the energy column diagram of EEG features related to the mental activity is presented to subjects with visual feedback in real time to train them to quickly master the skills of MI and regulate their EEG activity, and combination of multi-features fusion of MI and multi-classifiers decision were used to control the intelligent car online. The average, maximum and minimum accuracy of identifying instructions achieved by the trained group (trained by the designed feedback system before the experiment) were 85.71%, 90.47% and 76.19%, respectively and the corresponding accuracy achieved by the control group (untrained) were 73.32%, 80.95% and 66.67%, respectively. For the trained group, the average, longest and shortest time consuming were 92 s, 101 s, and 85 s, respectively, while for the control group the corresponding time were 115.7 s, 120 s, and 110 s, respectively. According to the results described above, it is expected that this study may provide a new idea for the follow-up development of brain-controlled intelligent robot by the neurofeedback with EEG related to MI.

An Empirical Evaluation of the Local Texture Description Framework-Based Modified Local Directional Number Pattern with Various Classifiers for Face Recognition

ABSTRACT Texture is one of the chief characteristics of an image. In recent years, local texture descriptors have garnered attention among researchers in describing effective texture patterns to demarcate facial images. A feature descriptor titled Local Texture Description Framework-based Modified Local Directional Number pattern (LTDF_MLDN), capable of encoding texture patterns with pixels that lie at dissimilar regions, has been proposed recently to describe effective features for face images. However, the role of the descriptor can differ with different classifiers and distance metrics for diverse issues in face recognition. Hence, in this paper, an extensive evaluation of the LTDF_MLDN is carried out with an Extreme Learning Machine (ELM), a Support Vector Machine (SVM) and a Nearest Neighborhood Classifier (NNC) which uses Euclidian, Manhattan, Minkowski, G-statistics and chi-square dissimilarity metrics to illustrate differences in performance with respect to assorted issues in face recognition using six benchmark databases. Experimental results depict that the proposed descriptor is best suited with NNC for general case and expression variation, whereas, for the other facial variations ELM is found to produce better results.

Algoritmos de aprendizaje automático para la clasificación de neuronas piramidales afectadas por el envejecimiento / Machine learning algorithms for classification of pyramidal neurons affected by aging

Una caracterización morfológica precisa de las múltiples clases neuronales del cerebro facilitaría la elucidación de la función cerebral y los cambios funcionales que subyacen a los trastornos neurológicos tales como enfermedades de Parkinson o la Esquizofrenia. El análisis morfológico manual es muy lento y sufre de falta de exactitud porque algunas características de las células no se cuantifican fácilmente. Este artículo presenta una investigación en la clasificación automática de un conjunto de neuronas piramidales de monos jóvenes y adultos, las cuales degradan su estructura morfológica con el envejecimiento. Un conjunto de 21 características se utilizaron para describir su morfología con el fin de identificar las diferencias entre las neuronas. En este trabajo se evalúa el desempeño de cuatro métodos de aprendizaje automático populares en la clasificación de árboles neuronales. Los métodos de aprendizaje de máquinas utilizadas son: máquinas de vectores soporte (SVM), k-vecinos más cercanos (KNN), regresión logística multinomial (MLR) y la red neuronal de propagación hacia atrás (BPNN). Los resultados mostraron las ventajas de MLR y BPNN con respecto a los demás para estos fines. Estos algoritmos de clasificación automáticaofrecen ventajas sobre la clasificación manualbasada en expertos.Mientras que la neurociencia está pasando rápidamente a datos digitales, los principios detrás de los algoritmos de clasificación automática permanecen a menudo inaccesibles para los neurocientíficos, lo que limita las posibilidades de avances(AU)

Accurate morphological characterization of the multiple neuronal classes of the brain would facilitate the elucidation of brain function and the functional changes that underlie neurological disorders such as Parkinson's diseases or Schizophrenia. Manual morphological analysis is very time-consuming and suffers from a lack of accuracy because some cell characteristics are not readily quantified. This paper presents an investigation in the automatic classification of a data set of pyramidal neurons of young and adult monkeys, which degrade his morphologic structure with the aging. A set of 21 features were used to describe their morphology in order to identify differences between neurons. Thispaper evaluates the performance of four popular machine learning methods, in the classification of neural trees. The machine learning methods used are: support vector machines (SVMs), k-nearest neighbors (KNN), multinomial logistic regression (MLR) and back propagation neural network (BPNN). The results showed the advantages of MLR and BPNN with respect to others for this purposes. These automatic classification algorithms offer advantages over manual expert based classification. While neuroscience is rapidly transitioning to digital data, the principles behind automatic classification algorithms remain often inaccessible to neuroscientists, limiting the potential for breakthroughs(AU)

Predication of Parkinson's disease using data mining methods: A comparative analysis of tree, statistical, and support vector machine classifiers.

The prediction of Parkinson's disease in early age has been challenging task among researchers, because the symptoms of disease came into existence in middle and late middle age. There are lots of symptoms that lead to Parkinson's disease. But this article focuses on the speech articulation difficulty symptoms of PD affected people and try to formulate the model on the behalf of three data mining methods. These three data mining methods are taken from three different domains of data mining i.e., from tree classifier, statistical classifier, and support vector machine classifier. Performance of these three classifiers is measured with three performance matrices i.e., accuracy, sensitivity, and specificity. Hence, the main task of this article is tried to find out which model identified the PD affected people more accurately.