scholarly journals Depression Detection Using Relative EEG Power Induced by Emotionally Positive Images and a Conformal Kernel Support Vector Machine

2018 ◽  
Vol 8 (8) ◽  
pp. 1244 ◽  
Author(s):  
Chien-Te Wu ◽  
Daniel Dillon ◽  
Hao-Chun Hsu ◽  
Shiuan Huang ◽  
Elyssa Barrick ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Resting State ◽  
Positive Emotion ◽  
Support Vector ◽  
Svm Classifier ◽  
Emotion Induction ◽  
Kernel Support Vector Machine ◽  
Eeg Power ◽  
Eeg Features

Electroencephalography (EEG) can assist with the detection of major depressive disorder (MDD). However, the ability to distinguish adults with MDD from healthy individuals using resting-state EEG features has reached a bottleneck. To address this limitation, we collected EEG data as participants engaged with positive pictures from the International Affective Picture System. Because MDD is associated with blunted positive emotions, we reasoned that this approach would yield highly dissimilar EEG features in healthy versus depressed adults. We extracted three types of relative EEG power features from different frequency bands (delta, theta, alpha, beta, and gamma) during the emotion task and resting state. We also applied a novel classifier, called a conformal kernel support vector machine (CK-SVM), to try to improve the generalization performance of conventional SVMs. We then compared CK-SVM performance with three machine learning classifiers: linear discriminant analysis (LDA), conventional SVM, and quadratic discriminant analysis. The results from the initial analyses using the LDA classifier on 55 participants (24 MDD, 31 healthy controls) showed that the participant-independent classification accuracy obtained by leave-one-participant-out cross-validation (LOPO-CV) was higher for the EEG recorded during the positive emotion induction versus the resting state for all types of relative EEG power. Furthermore, the CK-SVM classifier achieved higher LOPO-CV accuracy than the other classifiers. The best accuracy (83.64%; sensitivity = 87.50%, specificity = 80.65%) was achieved by the CK-SVM, using seven relative power features extracted from seven electrodes. Overall, combining positive emotion induction with the CK-SVM classifier proved useful for detecting MDD on the basis of EEG signals. In the future, this approach might be used to develop a brain–computer interface system to assist with the detection of MDD in the clinic. Importantly, such a system could be implemented with a low-density electrode montage (seven electrodes), highlighting its practical utility.

scholarly journals Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Fusun Citak-Er ◽  
Metin Vural ◽  
Omer Acar ◽  
Tarik Esen ◽  
Aslihan Onay ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Gleason Score ◽  
Specific Antigen ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Discriminant ◽  
Experienced Radiologist

Objective.This study aimed at evaluating linear discriminant analysis (LDA) and support vector machine (SVM) classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI) and clinical parameters.Materials and Methods.Thirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled in this study. The input features for classifiers were age, the presence of a palpable prostate abnormality, prostate specific antigen (PSA) level, index lesion size, and Likert scales of T2 weighted MRI (T2w-MRI), diffusion weighted MRI (DW-MRI), and dynamic contrast enhanced MRI (DCE-MRI) estimated by an experienced radiologist. SVM based recursive feature elimination (SVM-RFE) was used for eliminating features. Principal component analysis (PCA) was applied for data uncorrelation.Results.Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively. Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively.Conclusion.SVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population.

scholarly journals A Hybrid Fish – Bee Optimization Algorithm for Heart Disease Prediction using Multiple Kernel SVM Classifier

2019 ◽  
Vol 8 (9S2) ◽  
pp. 729-737
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Heart Disease ◽  
Optimization Algorithm ◽  
Selection Process ◽  
Support Vector ◽  
Svm Classifier ◽  
Disease Prediction ◽  
Kernel Support Vector Machine ◽  
Hybrid Fish

The patient’s heart disease status is obtained by using a heart disease detection model. That is used for the medical experts. In order to predict the heart disease, the existing technique use optimal classifier. Even though the existing technique achieved the better result, it has some disadvantages. In order to improve those drawbacks, the suggested technique utilizes the effective method for heart disease prediction. At first the input information is preprocessed and then the preprocessed result is forwarded to the feature selection process. For the feature selection process a proficient feature selection is used over the high dimensional medical data. Hybrid Fish Bee optimization algorithm (HFSBEE) is utilized. Thus, the proposed algorithm parallelizes the two algorithms such that the local behavior of artificial bee colony algorithm and global search of fish swarm optimization are effectively used to find the optimal solution. Classification process is performed by the transformation of medical dataset to the Multi kernel support vector machine (MKSVM). The process of our proposed technique is calculated based on the accuracy, sensitivity, specificity, precision, recall and F-measure. Here, for test analysis, the some datasets used i.e. Cleveland, Hungarian and Switzerland etc., that are given based on the UCI machine learning repository. The experimental outcome show that our presented technique is went better than the accuracy of 97.68%. This is for the Cleveland dataset when related with existing hybrid kernel support vector machine (HKSVM) method achieved 96.03% and optimal rough fuzzy classifier obtained 62.25%. The implementation of the proposed method is done by MATLAB platform.

scholarly journals Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features

2022 ◽  
Vol 19 (2) ◽  
pp. 629
Author(s):  
Yuting Wang ◽  
Shujian Wang ◽  
Ming Xu
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Classification Accuracy ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Landscape Perception ◽  
Landscape Types ◽  
Electroencephalogram Eeg ◽  
Eeg Features

This paper puts forward a new method of landscape recognition and evaluation by using aerial video and EEG technology. In this study, seven typical landscape types (forest, wetland, grassland, desert, water, farmland, and city) were selected. Different electroencephalogram (EEG) signals were generated through different inner experiences and feelings felt by people watching video stimuli of the different landscape types. The electroencephalogram (EEG) features were extracted to obtain the mean amplitude spectrum (MAS), power spectrum density (PSD), differential entropy (DE), differential asymmetry (DASM), rational asymmetry (RASM), and differential caudality (DCAU) in the five frequency bands of delta, theta, alpha, beta, and gamma. According to electroencephalogram (EEG) features, four classifiers including the back propagation (BP) neural network, k-nearest neighbor classification (KNN), random forest (RF), and support vector machine (SVM) were used to classify the landscape types. The results showed that the support vector machine (SVM) classifier and the random forest (RF) classifier had the highest accuracy of landscape recognition, which reached 98.24% and 96.72%, respectively. Among the six classification features selected, the classification accuracy of MAS, PSD, and DE with frequency domain features were higher than those of the spatial domain features of DASM, RASM and DCAU. In different wave bands, the average classification accuracy of all subjects was 98.24% in the gamma band, 94.62% in the beta band, and 97.29% in the total band. This study identifies and classifies landscape perception based on multi-channel EEG signals, which provides a new idea and method for the quantification of human perception.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

scholarly journals A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
HungLinh Ao ◽  
Junsheng Cheng ◽  
Kenli Li ◽  
Tung Khac Truong
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Roller Bearing ◽  
Local Characteristic ◽  
Support Vector ◽  
Svm Classifier ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Energy Entropy

This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

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