Classification of right and left hand movement using nonlinear analysis

EEG signals are non-stationary, complex and non-linear signals. During major depressive disorder (MDD) or depression, any deterioration in the brain function is reflected in the EEG signals. In this paper, linear features (band power, inter hemispheric asymmetry) and non-linear features [relative wavelet energy (RWE) and wavelet entropy (WE)] and combination of linear and non-linear features were used to classify depression patients and healthy individuals. In this analysis the data set used is publicly available data set contributed by Mumtaz et al. (Biomed Signal Process Control 31:108–115, 2017b). The dataset consisted of 34 MDD patients and 30 healthy individuals. The classifiers used were multi layered perceptron neural network (MLPNN), radial basis function network (RBFN), linear discriminant analysis (LDA) and quadratic discriminant analysis. When linear feature was used, highest classification accuracy of 91.67% was obtained by alpha power with MLPNN classifier. When non-linear feature was used, both RWE and WE provided highest classification accuracy of 90% with RBFN and LDA classifier, respectively. The highest classification of 93.33% was achieved when combining linear and non-linear feature, i.e., combination alpha power and RWE with MLPNN as well as RBFN classifier. This paper also showed that the combination of non-linear features, i.e., RWE and WE also performed the best with highest classification accuracy of 93.33%. The study compared the accuracy, sensitivity and specificity of different classifiers along with linear and non-linear features and combination of both. The results indicated that combination alpha power and RWE showed the highest classification 93.33% accuracy in all the applied classifiers.

There are many stressful environments which deteriorate the performance of speech recognition systems. Examples include aircraft cockpits, 911 emergency telephone response, high workload task stress, or emotional situations. To address this, we investigate a number of linear and nonlinear features and processing methods for stressed speech classi cation. The linear features include properties of pitch, duration, intensity, glottal source, and the vocal tract spectrum. Nonlinear processing is based on our newly proposed Teager Energy Operator (TEO) speech feature which incorporates frequency domain critical band lters and properties of the resulting TEO autocorrelation envelope. In this study, we employ a Bayesian hypothesis testing approach and a hidden Markov model (HMM) processor as classi cation methods. Evaluations focused on speech under loud, angry, and the Lombard e ect1 from the SUSAS database. Results using receiver operating characteristic (ROC) curves and EER (equal error rate) based detection show that pitch is the best of the ve linear features for stress classi cation; while the new nonlinear TEO-based feature outperforms the best linear feature by +5.2%, with a reduction in classi cation rate variability from 8.66 to 3.90.

EEG-based mild depressive detection using feature selection methods and classifiers

The present research is about the various mental tasks, experienced by humans with cognitive function disorders, classified using discrete wavelet transform (DWT) and support vector machine (SVM). The electroencephalogram (EEG) database was obtained from online brain-computer interface (BCI) competition paradigm III and offline B-alert EEG machine was from CARE Hospital, Nagpur. EEG signals from paralysed patients decomposed into the frequency sub-bands using DWT and a set of statistical features extracted from the sub-bands represent the distribution of wavelet coefficients used to reduce the dimension of data, features applied to SVM for classification of left hand and right hand movement. With this system, classification of EEG signals was done with accuracy 91.66% for BCI competition paradigm III and 97% for B-alert machine.

In this study, we aimed to recognize six hand motions using surface electromyogram (sEMG) signals recorded from eight muscles of the right hand. Twenty-five healthy subjects participated in this experiment. Data were segmented using windows of 250-ms length with a 150-ms overlapping. In this paper, we extracted 24 features per muscle. Three feature sets-the original features, the features produced by a discriminant analysis (DA) and those selected by a multiple regression analysis (MRA) entered into one of the following classifiers: linear discriminant analysis (LDA) or support vector machine (SVM). The results showed that the original features classified by the SVM reached an average accuracy of 91.2 ± 0.383 %, significantly higher than the other approaches. The index finger extension (IFE) had higher classification accuracy than the other hand motions. The probability of the thumb opposition (TO) falsely classified as key pinch (KP) was 1.1 %, that of the hand grasp (HG) falsely classified as four fingers flexion (FFF) was 1.0 %.

Extraction of potential electromyography (EMG) features has become one of the important roles in EMG pattern recognition. In this paper, two EMG features, namely, enhanced wavelength (EWL) and enhanced mean absolute value (EMAV) are proposed. The EWL and EMAV are the modified version of wavelength (WL) and mean absolute value (MAV), which aims to enhance the prediction accuracy for the classification of hand movements. Initially, the proposed features are extracted from the EMG signals via discrete wavelet transform (DWT). The extracted features are then fed into the machine learning algorithm for classification process. Four popular machine learning algorithms include k-nearest neighbor (KNN), linear discriminate analysis (LDA), Naïve Bayes (NB) and support vector machine (SVM) are used in evaluation. To examine the effectiveness of EWL and EMAV, several conventional EMG features are used in performance comparison. In addition, the efficacy of EWL and EMAV when combine with other features are also investigated. Based on the results obtained, the combination of EWL and EMAV with other features can improve the classification performance. Thus, EWL and EMAV can be considered as valuable tools for rehabilitation and clinical applications.

Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features

Microbes play a crucial role in human health and disease. Understanding the relationship between microbes and diseases is conducive to the treatment and diagnosis of diseases. Recently, many computational methods have been proposed to predict disease-microbe associations. However, most of the existing methods only consider a single model and explore the disease-microbe associations from a single view. To improve the prediction accuracy, we propose a novel multi-view approach that fuses the linear and nonlinear features to predict new potential associations between diseases and microbes. We first design a non-negative matrix tri-factorization method to extract the linear features of diseases and microbes. We input the linear features from the non-negative matrix tri-factorization model and bi-random walk model into a capsule neural network to obtain the diseases and microbes’ nonlinear features. Finally, we leverage a logistic regression model to combine the non-negative matrix tri-factorization model predictions, bi-random walk model predictions and the capsule neural network predictions to obtain the final association scores between microbes and diseases. We apply our method to predict human microbedisease associations. Experimental results show that our fusion model outperforms the non-negative matrix tri-factorization model, bi-random walk model and other existing models.

A query image based scene/image retrieval system is a system that analyzes the properties of a query image and identifies the class in which the image belongs and retrieves a number of images which are most alike and relevant to the query image. A scene/image classifier provides the first stage for this system. Scene classification is the process that analyzes the properties of various image features of a scene and organizes the scenes into categories. Programmed techniques for correlating scenes with labels have a high potential for improving the performance of computer vision applications such as searching, retrieval and object recognition. The scene category system uses the feature vectors extracted from image data to classify test images. We have implemented a k-nearest neighbour (kNN) classifier which uses gray scale and Scale Invariant Feature Transform (SIFT) features as input and used support vector machine (SVM) classifier that uses GIST features as input. kNN classifier is used, as it has an advantage of working with a large amount of data and SVM is robust even when the training sample has some bias. The performance measure of the classifier was evaluated using confusion matrix and classification accuracy. We considered MIT-8 scene category, leaves, Caltech-20 and deer data sets for our experiments. The classification accuracy was found to be good. Results show that classification accuracy is increased significantly for classification using GIST features in SVM classifiers. We are exploring methods to obtain optimal performance of classifiers.

The problem with development of the support vector machine (SVM) classifiers using modified particle swarm optimization (PSO) algorithm and their ensembles has been considered. Solving this problem would allow fulfilling the high-precision data classification, especially Big Data classification, with the acceptable time expenditures. The modified PSO algorithm conducts a simultaneous search of the type of kernel functions, the parameters of the kernel function and the value of the regularization parameter for the SVM classifier. The idea of particles' «regeneration» served as the basis for the modified PSO algorithm. In the implementation of this algorithm, some particles change the type of their kernel function to the one which corresponds to the particle with the best value of the classification accuracy. The offered PSO algorithm allows reducing the time expenditures for the developed SVM classifiers, which is very important for Big Data classification problem. In most cases such SVM classifier provides the high quality of data classification. In exceptional cases the SVM ensembles based on the decorrelation maximization algorithm for the different strategies of the decision-making on the data classification and the majority vote rule can be used. Also, the two-level SVM classifier has been offered. This classifier works as the group of the SVM classifiers at the first level and as the SVM classifier on the base of the modified PSO algorithm at the second level. The results of experimental studies confirm the efficiency of the offered approaches for Big Data classification.

Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis

One of the most serious problems that faces human nowadays is the gait disturbances as result of neurodegenerative diseases (NDD). Neurodegenerative diseases such as Parkinson's disease (PD), Amyotrophic Lateral Sclerosis (ALS), Huntington Disease (HD) identified as the dynamic loss of neurons in human brain. Therefore, gait analysis can yield a significant approach for the early diagnosis of gait disturbances and determine the treatment plan with the generation of new era of computerized medical systems for analyzing such diseases. The present study explores the improvement of the classification capability by using non-linear features with previously used linear features. Fisher score selection strategy is used to get the optimal feature subset and the optimal gait time series in classifying NDD. Support vector machine (SVM) with radial basis kernel function (RBF) is implemented for discriminating NDD patients against healthy ones optimized by leave-one-out-cross-validation (LOOCV). The applied classifier differentiated NDD subjects from healthy ones with an area under the receiver operating characteristic curve “0.861” and an overall accuracy “90.625%”.

Detecting epileptic seizure onsets is the main goal of numerous studies, since it has many profits for patients and clinicians. Methods based on electroencephalogram (EEG), electrocardiogram (ECG), and other electrophysiological signals had been used for automatic detection in the literature. For the first time, absence seizures have been detected based on ECG signals in this study. Animal models of absence epilepsy, WAG\Rij rats, with repetitive seizures (duration about few seconds'), have been investigated. After detecting QRS complexes from ECG signal and extracting 38 different linear, nonlinear and frequency domain features from heart rate variability, feature vectors were constructed. In order to obtain high efficiency detection algorithm, feature selection have been implemented based on wrapper approach. Results related to support vector machine (SVM), linear discriminate analysis (LDA), and k-nearest neighbor (kNN), three important classifiers for seizure detection have been compared in this work. The test results for patient- independent detection with 5 selected features in leave-one-out (LOO) train approach had accuracy of 74%, 72% and 71% for SVM, LDA and kNN, respectively. All the algorithms and methods have been optimized to be useful in embedded implementations.

Insomnia is a sleep disorder that causes disturbance in a normal sleep pattern resulting from the difficulties to fall asleep or to stay asleep. Biosignals of human body if measured are also affected due to these abnormal conditions. A current practise in diagnosing insomnia is through clinical interview by the physician which is subjective and suffers from human error judgement. Therefore, a more reliable and accurate diagnostic tools are needed to help physician in making decision. The objective of this study is to classify healthy and insomnia by implementing advanced classification technique based on Artificial Neural Network (ANN).In this study, sleep EEG and ECG signals of 10 insomnia patients and 10 healthy subjects are analysed. Several linear and nonlinear features are extracted from the denoised signals: linear features (power spectral of EEG frequency bands, brain rate, Hjorth parameters, heart rate variability) and nonlinear features (Largest Lyapunov Exponent (LLE), Sample Entropy and Correlation Dimension (CD)).For classification purpose, a Feedforward Neural Network (FNN) is implemented to classify the two groups. Half of the data is used for training and the other half is used for testing the classifier. The Levenberg Marquardt backpropagation algorithm is used as a training function. Several numbers of hidden layers are tested in order to achieve optimum classification accuracy. A classification accuracy of 81.3 % is obtained for 3 hidden layers This result shows that the combination of features extracted from EEG and ECG signals during sleep and FFN are useful to be adopted as a biomarker and classifier in identifying insomnia.of FNN.

In the context of improved navigation for micro aerial vehicles, a new scene recognition visual descriptor, called spatial color gist wavelet descriptor (SCGWD), is proposed. SCGWD was developed by combining proposed Ohta color-GIST wavelet descriptors with census transform histogram (CENTRIST) spatial pyramid representation descriptors for categorizing indoor versus outdoor scenes. A binary and multiclass support vector machine (SVM) classifier with linear and non-linear kernels was used to classify indoor versus outdoor scenes and indoor scenes, respectively. In this paper, we have also discussed the feature extraction methodology of several, state-of-the-art visual descriptors, and four proposed visual descriptors (Ohta color-GIST descriptors, Ohta color-GIST wavelet descriptors, enhanced Ohta color histogram descriptors, and SCGWDs), in terms of experimental perspectives. The proposed enhanced Ohta color histogram descriptors, Ohta color-GIST descriptors, Ohta color-GIST wavelet descriptors, SCGWD, and state-of-the-art visual descriptors were evaluated, using the Indian Institute of Technology Madras Scene Classification Image Database two, an Indoor-Outdoor Dataset, and the Massachusetts Institute of Technology indoor scene classification dataset [(MIT)-67]. Experimental results showed that the indoor versus outdoor scene recognition algorithm, employing SVM with SCGWDs, produced the highest classification rates (CRs)—95.48% and 99.82% using radial basis function kernel (RBF) kernel and 95.29% and 99.45% using linear kernel for the IITM SCID2 and Indoor-Outdoor datasets, respectively. The lowest CRs—2.08% and 4.92%, respectively—were obtained when RBF and linear kernels were used with the MIT-67 dataset. In addition, higher CRs, precision, recall, and area under the receiver operating characteristic curve values were obtained for the proposed SCGWDs, in comparison with state-of-the-art visual descriptors.