scholarly journals Recognition of Imbalanced Epileptic EEG Signals by a Graph-Based Extreme Learning Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Zhou ◽  
Xiongtao Zhang ◽  
Zhibin Jiang
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Eeg Signal ◽  
Signal Recognition ◽  
Eeg Signals ◽  
Learning Methods ◽  
Imbalanced Classification ◽  
Machine Learning Methods ◽  
Learning Machine

Epileptic EEG signal recognition is an important method for epilepsy detection. In essence, epileptic EEG signal recognition is a typical imbalanced classification task. However, traditional machine learning methods used for imbalanced epileptic EEG signal recognition face many challenges: (1) traditional machine learning methods often ignore the imbalance of epileptic EEG signals, which leads to misclassification of positive samples and may cause serious consequences and (2) the existing imbalanced classification methods ignore the interrelationship between samples, resulting in poor classification performance. To overcome these challenges, a graph-based extreme learning machine method (G-ELM) is proposed for imbalanced epileptic EEG signal recognition. The proposed method uses graph theory to construct a relationship graph of samples according to data distribution. Then, a model combining the relationship graph and ELM is constructed; it inherits the rapid learning and good generalization capabilities of ELM and improves the classification performance. Experiments on a real imbalanced epileptic EEG dataset demonstrated the effectiveness and applicability of the proposed method.

scholarly journals Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 210 ◽  
Author(s):  
Zied Tayeb ◽  
Juri Fedjaev ◽  
Nejla Ghaboosi ◽  
Christoph Richter ◽  
Lukas Everding ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Classification Performance ◽  
Feature Engineering ◽  
Learning Models ◽  
Eeg Signals ◽  
Learning Methods

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

scholarly journals Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection of Aphis gossypii Glover Infection in Cotton Leaves Using Hyperspectral Imaging

2021 ◽  
Vol 12 ◽  
Author(s):  
Tianying Yan ◽  
Wei Xu ◽  
Jiao Lin ◽  
Long Duan ◽  
Pan Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hyperspectral Imaging ◽  
Aphis Gossypii ◽  
Classification Performance ◽  
Hyperspectral Images ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Conventional Machine ◽  
Rgb Images ◽  
3D Cnn

Cotton is a significant economic crop. It is vulnerable to aphids (Aphis gossypii Glovers) during the growth period. Rapid and early detection has become an important means to deal with aphids in cotton. In this study, the visible/near-infrared (Vis/NIR) hyperspectral imaging system (376–1044 nm) and machine learning methods were used to identify aphid infection in cotton leaves. Both tall and short cotton plants (Lumianyan 24) were inoculated with aphids, and the corresponding plants without aphids were used as control. The hyperspectral images (HSIs) were acquired five times at an interval of 5 days. The healthy and infected leaves were used to establish the datasets, with each leaf as a sample. The spectra and RGB images of each cotton leaf were extracted from the hyperspectral images for one-dimensional (1D) and two-dimensional (2D) analysis. The hyperspectral images of each leaf were used for three-dimensional (3D) analysis. Convolutional Neural Networks (CNNs) were used for identification and compared with conventional machine learning methods. For the extracted spectra, 1D CNN had a fine classification performance, and the classification accuracy could reach 98%. For RGB images, 2D CNN had a better classification performance. For HSIs, 3D CNN performed moderately and performed better than 2D CNN. On the whole, CNN performed relatively better than conventional machine learning methods. In the process of 1D, 2D, and 3D CNN visualization, the important wavelength ranges were analyzed in 1D and 3D CNN visualization, and the importance of wavelength ranges and spatial regions were analyzed in 2D and 3D CNN visualization. The overall results in this study illustrated the feasibility of using hyperspectral imaging combined with multi-dimensional CNN to detect aphid infection in cotton leaves, providing a new alternative for pest infection detection in plants.

Quantitative Assessment of Ataxia in Multiple Sclerosis Patients using Spatiotemporal Parameters: A Relief-Based Machine Learning Analysis

2021 ◽  
Author(s):  
Furkan Bilek ◽  
Ferhat Balgetir ◽  
Caner Feyzi Demir ◽  
Gökhan Alkan ◽  
Seda Arslan-Tuncer
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Sensitivity And Specificity ◽  
Classification Performance ◽  
Support Vector ◽  
Learning Methods ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Spatiotemporal Parameters ◽  
Multiple Sclerosis Patients

Abstract Background and Objective Multiple sclerosis (MS) is a chronic, progressive, and autoimmune disease of the central nervous system (CNS) characterized by inflammation, demyelination, and axonal injury. In patients with newly diagnosed MS (ndMS), ataxia can present either as mild or severe and can be difficult to diagnose in the absence of clinical disability. Such difficulties can be eliminated by using decision support systems supported by machine learning methods. The present study aimed to achieve early diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. Materials and Methods The prospective study included 32 ndMS patients with an Expanded Disability Status Scale (EDSS) score of≤2.0 and 32 healthy volunteers. A total of 14 parameters were elicited by using a Win-Track platform. The ndMS patients were differentiated from healthy individuals using multiple classifiers including Artificial Neural Network (ANN), Support Vector Machine (SVM), the k-nearest neighbors (K-NN) algorithm, and Decision Tree Learning (DTL). To improve the performance of the classification, a Relief-based feature selection algorithm was applied to select the subset that best represented the whole dataset. Performance evaluation was achieved based on several criteria such as Accuracy (ACC), Sensitivity (SN), Specificity (SP), and Precision (PREC). Results ANN had a higher classification performance compared to other classifiers, whereby it provided an accuracy, sensitivity, and specificity of 89, 87.8, 90.3% with the use of all parameters and provided the values of 93.7, 96.6%, and 91.1% with the use of parameters selected by the Relief algorithm, respectively. Significance To our knowledge, this is the first study of its kind in the literature to investigate the diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. The proposed method, i. e. Relief-based ANN method, successfully diagnosed ataxia by using a lower number of parameters compared to the numbers of parameters reported in clinical studies, thereby reducing the costs and increasing the performance of the diagnosis. The method also provided higher rates of accuracy, sensitivity, and specificity in the diagnosis of ataxia in ndMS patients compared to other methods. Taken together, these findings indicate that the proposed method could be helpful in the diagnosis of ataxia in minimally impaired ndMS patients and could be a pathfinder for future studies.

scholarly journals Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3085 ◽  
Author(s):  
Raluca Brehar ◽  
Delia-Alexandrina Mitrea ◽  
Flaviu Vancea ◽  
Tiberiu Marita ◽  
Sergiu Nedevschi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Binary Classification ◽  
Classification Performance ◽  
Ultrasound Images ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Segmentation Accuracy ◽  
Conventional Machine

The emergence of deep-learning methods in different computer vision tasks has proved to offer increased detection, recognition or segmentation accuracy when large annotated image datasets are available. In the case of medical image processing and computer-aided diagnosis within ultrasound images, where the amount of available annotated data is smaller, a natural question arises: are deep-learning methods better than conventional machine-learning methods? How do the conventional machine-learning methods behave in comparison with deep-learning methods on the same dataset? Based on the study of various deep-learning architectures, a lightweight multi-resolution Convolutional Neural Network (CNN) architecture is proposed. It is suitable for differentiating, within ultrasound images, between the Hepatocellular Carcinoma (HCC), respectively the cirrhotic parenchyma (PAR) on which HCC had evolved. The proposed deep-learning model is compared with other CNN architectures that have been adapted by transfer learning for the ultrasound binary classification task, but also with conventional machine-learning (ML) solutions trained on textural features. The achieved results show that the deep-learning approach overcomes classical machine-learning solutions, by providing a higher classification performance.

scholarly journals Reproductive phasiRNAs in grasses are compositionally distinct from other classes of small RNAs

2018 ◽  
Author(s):  
Parth Patel ◽  
Sandra Mathioni ◽  
Atul Kakrana ◽  
Hagit Shatkay ◽  
Blake C. Meyers
Keyword(s):  
Machine Learning ◽  
Small Rnas ◽  
Structural Features ◽  
Classification Performance ◽  
List Type ◽  
Small Interfering Rnas ◽  
Sequencing Data ◽  
Learning Methods ◽  
Specific Sequence ◽  
Machine Learning Methods

Summary and keywordsLittle is known about the characteristics and function of reproductive phased, secondary, small interfering RNAs (phasiRNAs) in the Poaceae, despite the availability of significant genomic resources, experimental data, and a growing number of computational tools. We utilized machine-learning methods to identify sequence-based and structural features that distinguish phasiRNAs in rice and maize from other small RNAs (sRNAs).We developed Random Forest classifiers that can distinguish reproductive phasiRNAs from other sRNAs in complex sets of sequencing data, utilizing sequence-based (k-mers) and features describing position-specific sequence biases.The classification performance attained is >80% in accuracy, sensitivity, specificity, and positive predicted value. Feature selection identified important features in both ends of phasiRNAs. We demonstrated that phasiRNAs have strand specificity and position-specific nucleotide biases potentially influencing AGO sorting; we also predicted targets to infer functions of phasiRNAs, and computationally-assessed their sequence characteristics relative to other sRNAs.Our results demonstrate that machine-learning methods effectively identify phasiRNAs despite the lack of characteristic features typically present in precursor loci of other small RNAs, such as sequence conservation or structural motifs. The 5’-end features we identified provide insights into AGO-phasiRNA interactions; we describe a hypothetical model of competition for AGO loading between phasiRNAs of different nucleotide compositions.

scholarly journals Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 415 ◽  
Author(s):  
Bomi Jeong ◽  
Hyunjeong Cho ◽  
Jieun Kim ◽  
Soon Kil Kwon ◽  
SeungWoo Hong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Statistical Models ◽  
Classification Performance ◽  
Health Examination ◽  
Learning Methods ◽  
Response Variable ◽  
Machine Learning Methods ◽  
Sensitivity Specificity ◽  
Weighted Values

This study aims to compare the classification performance of statistical models on highly imbalanced kidney data. The health examination cohort database provided by the National Health Insurance Service in Korea is utilized to build models with various machine learning methods. The glomerular filtration rate (GFR) is used to diagnose chronic kidney disease (CKD). It is calculated using the Modification of Diet in Renal Disease method and classified into five stages (1, 2, 3A and 3B, 4, and 5). Different CKD stages based on the estimated GFR are considered as six classes of the response variable. This study utilizes two representative generalized linear models for classification, namely, multinomial logistic regression (multinomial LR) and ordinal logistic regression (ordinal LR), as well as two machine learning models, namely, random forest (RF) and autoencoder (AE). The classification performance of the four models is compared in terms of accuracy, sensitivity, specificity, precision, and F1-Measure. To find the best model that classifies CKD stages correctly, the data are divided into a 10-fold dataset with the same rate for each CKD stage. Results indicate that RF and AE show better performance in accuracy than the multinomial and ordinal LR models when classifying the response variable. However, when a highly imbalanced dataset is modeled, the accuracy of the model performance can distort the actual performance. This occurs because accuracy is high even if a statistical model classifies a minority class into a majority class. To solve this problem in performance interpretation, we not only consider accuracy from the confusion matrix but also sensitivity, specificity, precision, and F-1 measure for each class. To present classification performance with a single value for each model, we calculate the macro-average and micro-weighted values for each model. We conclude that AE is the best model classifying CKD stages correctly for all performance indices.

scholarly journals Multimodal Emotion Recognition Using the Symmetric S-ELM-LUPI Paradigm

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 487
Author(s):  
Lingzhi Yang ◽  
Xiaojuan Ban ◽  
Michele Mukeshimana ◽  
Zhe Chen
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Extreme Learning Machine ◽  
High Speed ◽  
Information Source ◽  
Privileged Information ◽  
Learning Methods ◽  
Multimodal Emotion Recognition ◽  
Learning Machine ◽  
Learning Using Privileged Information

Multimodal emotion recognition has become one of the new research fields of human-machine interaction. This paper focuses on feature extraction and data fusion in audio-visual emotion recognition, aiming at improving recognition effect and saving storage space. A semi-serial fusion symmetric method is proposed to fuse the audio and visual patterns of emotional recognition, and a method of Symmetric S-ELM-LUPI is adopted (Symmetric Sparse Extreme Learning Machine-Learning Using Privileged Information). The method inherits the generalized high speed of the Extreme Learning Machine, and combines this with the acceleration in the recognition process by the Learning Using Privileged Information and the memory saving of the Sparse Extreme Learning Machine. It is a learning method, which improves the traditional learning methods of examples and targets only. It introduces the role of a teacher in providing additional information to enhance the recognition (test) without complicating the learning process. The proposed method is tested on publicly available datasets and yields promising results. This method regards one pattern as the standard information source, while the other pattern as the privileged information source. Each mode can be treated as privileged information for another mode. The results show that this method is appropriate for multi-modal emotion recognition. For hundreds of samples, the execution time is less than one percent seconds. The sparsity of the proposed method has the advantage of storing memory economy. Compared with other machine learning methods, this method is more accurate and stable.

Grasp Recognition for Uncalibrated Data Gloves: A Machine Learning Approach

2008 ◽  
Vol 17 (2) ◽  
pp. 121-142 ◽  
Author(s):  
Guido Heumer ◽  
Heni Ben Amor ◽  
Bernhard Jung
Keyword(s):  
Machine Learning ◽  
Principal Component ◽  
Classification Performance ◽  
Learning Methods ◽  
Data Glove ◽  
Machine Learning Methods ◽  
Machine Learning Approach ◽  
Meta Learning ◽  
Different Shapes ◽  
Selection Of

This paper presents a comparison of various machine learning methods applied to the problem of recognizing grasp types involved in object manipulations performed with a data glove. Conventional wisdom holds that data gloves need calibration in order to obtain accurate results. However, calibration is a time-consuming process, inherently user-specific, and its results are often not perfect. In contrast, the present study aims at evaluating recognition methods that do not require prior calibration of the data glove. Instead, raw sensor readings are used as input features that are directly mapped to different categories of hand shapes. An experiment was carried out in which test persons wearing a data glove had to grasp physical objects of different shapes corresponding to the various grasp types of the Schlesinger taxonomy. The collected data was comprehensively analyzed using numerous classification techniques provided in an open-source machine learning toolbox. Evaluated machine learning methods are composed of (a) 38 classifiers including different types of function learners, decision trees, rule-based learners, Bayes nets, and lazy learners; (b) data preprocessing using principal component analysis (PCA) with varying degrees of dimensionality reduction; and (c) five meta-learning algorithms under various configurations where selection of suitable base classifier combinations was informed by the results of the foregoing classifier evaluation. Classification performance was analyzed in six different settings, representing various application scenarios with differing generalization demands. The results of this work are twofold: (1) We show that a reasonably good to highly reliable recognition of grasp types can be achieved—depending on whether or not the glove user is among those training the classifier—even with uncalibrated data gloves. (2) We identify the best performing classification methods for the recognition of various grasp types. To conclude, cumbersome calibration processes before productive usage of data gloves can be spared in many situations.

Sign in / Sign up

Export Citation Format

Share Document