scholarly journals Epileptic Seizure Data Classification Using RBAs and Linear SVM

2019 ◽  
Vol 12 (2) ◽  
pp. 549-562
Author(s):  
Alpika Tripathi ◽  
Geetika Srivastava ◽  
K.K. Singh ◽  
P.K. Maurya
Keyword(s):  
Computational Cost ◽  
High Sensitivity ◽  
Support Vector ◽  
Medical Sciences ◽  
Hilbert Huang Transform ◽  
Relief Algorithm ◽  
Eeg Data ◽  
Linear Svm ◽  
Sensitivity Specificity

The objective of this paper is to make a distinction between EEG data of normal and epileptic subjects. Methods: The dataset is taken from 20-30 years healthy male/female subjects from EEG lab of Dept. of Neurology, Dr. RML Institute of Medical Sciences, Lucknow (India). The feature extraction has been done using the Hilbert Huang Transform (HHT) method. The experimental EEG signals have been decomposed till 5th level of Intrinsic Mode Function (IMF) followed by calculation of high order statistical values of each IMF. Relief algorithm (RBAs) is used for feature selection and classification is performed using Linear Support Vector Machine (Linear SVM). This paper gives an independent approach of classifying Epileptic EEG data with reduced computational cost and high accuracy. Our classification result shows sensitivity, specificity, selectiv­ity and accuracy of 96.4%, 79.16%, 84.3% and 88.5% respectively. The proposed method has been analyzed to be very effective in accurate classification of epileptic EEG data with high sensitivity.

scholarly journals Using AI-Based Classification Techniques to Process EEG Data Collected during the Visual Short-Term Memory Assessment

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Milos Antonijevic ◽  
Miodrag Zivkovic ◽  
Sladjana Arsic ◽  
Aleksandar Jevremovic
Keyword(s):  
Visual Information ◽  
Short Term Memory ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Memory Assessment ◽  
Visual Short Term Memory ◽  
Eeg Data ◽  
Short Period

Visual short-term memory (VSTM) is defined as the ability to remember a small amount of visual information, such as colors and shapes, during a short period of time. VSTM is a part of short-term memory, which can hold information up to 30 seconds. In this paper, we present the results of research where we classified the data gathered by using an electroencephalogram (EEG) during a VSTM experiment. The experiment was performed with 12 participants that were required to remember as many details as possible from the two images, displayed for 1 minute. The first assessment was done in an isolated environment, while the second assessment was done in front of the other participants, in order to increase the stress of the examinee. The classification of the EEG data was done by using four algorithms: Naive Bayes, support vector, KNN, and random forest. The results obtained show that AI-based classification could be successfully used in the proposed way, since we were able to correctly classify the order of the images presented 90.12% of the time and type of the displayed image 90.51% of the time.

scholarly journals Supervised Learning Based Classification of Cardiovascular Diseases

2021 ◽  
Vol 20 ◽  
pp. 24-34
Author(s):  
Arif Hussain ◽  
Hassaan Malik ◽  
Muhammad Umar Chaudhry
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Matthews Correlation Coefficient ◽  
Early Stage ◽  
Support Vector ◽  
Logistics Regression ◽  
Efficiency And Effectiveness ◽  
Sensitivity Specificity ◽  
Better Than

Detecting cardiovascular disease (CVD) in the early stage is a difficult and crucial process. The objective of this study is to test the capability of machine learning (ML) methods for accurately diagnosing the CVD outcomes. For this study, the efficiency and effectiveness of four well renowned ML classifiers, i.e., support vector machine (SVM), logistics regression (LR), naive Bayes (NB), and decision tree (J48), are measured in terms of precision, sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), correctly and incorrectly classified instances, and model building time. These ML classifiers are applied on publically available CVD dataset. In accordance with the measured result, J48 performs better than its competitor classifiers, providing significant assistance to the cardiologists.

scholarly journals Three simple steps to improve the interpretability of EEG-SVM studies

2021 ◽  
Author(s):  
Coralie Joucla ◽  
Damien Gabriel ◽  
Emmanuel Haffen ◽  
Juan-Pablo Ortega
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Research Literature ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Diagnosis And Prognosis ◽  
Eeg Data ◽  
Clinical Adoption

Research in machine-learning classification of electroencephalography (EEG) data offers important perspectives for the diagnosis and prognosis of a wide variety of neurological and psychiatric conditions, but the clinical adoption of such systems remains low. We propose here that much of the difficulties translating EEG-machine learning research to the clinic result from consistent inaccuracies in their technical reporting, which severely impair the interpretability of their often-high claims of performance. Taking example from a major class of machine-learning algorithms used in EEG research, the support-vector machine (SVM), we highlight three important aspects of model development (normalization, hyperparameter optimization and cross-validation) and show that, while these 3 aspects can make or break the performance of the system, they are left entirely undocumented in a shockingly vast majority of the research literature. Providing a more systematic description of these aspects of model development constitute three simple steps to improve the interpretability of EEG-SVM research and, in fine, its clinical adoption.

Classification of non-chronic and chronic kidney disease using SVM neural networks

2017 ◽  
Vol 7 (1.3) ◽  
pp. 191 ◽  
Author(s):  
Ravindra B.V ◽  
N Sriraam ◽  
M Geetha
Keyword(s):  
Neural Networks ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Classification Accuracy ◽  
The Body ◽  
Support Vector ◽  
Potential Candidate ◽  
Kidney Dialysis ◽  
Sensitivity Specificity

Chronic kidney disease (CKD) refers to the failure of the renal functionalities that leads to the deposition of wastes, electrolytes and other fluids in the body. It is very important to recognize the symptoms that cause the CKD and pathological blood and urine test indicates the key attributes. It is well fact that one has to undergo dialysis due to renal failure. The severity level of disease can be predicted as well as classified using appropriate computer aided quantitative tools. This specific study discusses the classification of chronic and non-chronic kidney disease NCKD using support vector machine (SVM) neural networks. The simulation study makes use of UCI repository CKD datasets with n=400. In order to train to train the attributes of kidney dialysis four cases were considered by including the nominal and numerical values. A radical basis kernel function was employed to train SVM. The performance of the proposed scheme is evaluated in terms of the sensitivity, specificity and classification accuracy. Results reveal an overall classification accuracy of 94.44% was obtained by combining 6 attributes. It can be concluded that the SVM based approach found to be a potential candidate for classification of CKD and NCKD.

scholarly journals Automated Recognition of Epileptic EEG States Using a Combination of Symlet Wavelet Processing, Gradient Boosting Machine, and Grid Search Optimizer

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 219 ◽  
Author(s):  
Xiashuang Wang ◽  
Guanghong Gong ◽  
Ni Li
Keyword(s):  
Classification Accuracy ◽  
Classification Scheme ◽  
Normal Subjects ◽  
Gradient Boosting ◽  
Support Vector ◽  
Grid Search ◽  
Automated Recognition ◽  
Eeg Data ◽  
Gradient Boosting Machine

Automatic recognition methods for non-stationary electroencephalogram (EEG) data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-class classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets are adopted to decompose the EEG data into five frequencies sub-bands, such as gamma, beta, alpha, theta, and delta, whose statistical features were computed and used as classification features. The grid search optimizer is used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a conventional support vector machine and a random forest classifier constructed according to previous descriptions. Multiple performance indices were used to evaluate the proposed classification scheme, which provided better classification accuracy and detection effectiveness than has been recently reported in other studies on three-class classification of EEG data.

scholarly journals A novel strategy for driving car brain–computer interfaces: Discrimination of EEG-based visual-motor imagery

2021 ◽  
Vol 12 (1) ◽  
pp. 482-493
Author(s):  
Zhouzhou Zhou ◽  
Anmin Gong ◽  
Qian Qian ◽  
Lei Su ◽  
Lei Zhao ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Small Sample ◽  
Support Vector ◽  
Hilbert Huang Transform ◽  
Sample Classification ◽  
Eeg Data ◽  
Visual Motor ◽  
Novel Strategy

Abstract A brain–computer interface (BCI) based on kinesthetic motor imagery has a potential of becoming a groundbreaking technology in a clinical setting. However, few studies focus on a visual-motor imagery (VMI) paradigm driving BCI. The VMI-BCI feature extraction methods are yet to be explored in depth. In this study, a novel VMI-BCI paradigm is proposed to execute four VMI tasks: imagining a car moving forward, reversing, turning left, and turning right. These mental strategies can naturally control a car or robot to move forward, backward, left, and right. Electroencephalogram (EEG) data from 25 subjects were collected. After the raw EEG signal baseline was corrected, the alpha band was extracted using bandpass filtering. The artifacts were removed by independent component analysis. Then, the EEG average instantaneous energy induced by VMI (VMI-EEG) was calculated using the Hilbert–Huang transform (HHT). The autoregressive model was extracted to construct a 12-dimensional feature vector to a support vector machine suitable for small sample classification. This was classified into two-class tasks: visual imagination of driving the car forward versus reversing, driving forward versus turning left, driving forward versus turning right, reversing versus turning left, reversing versus turning right, and turning left versus turning right. The results showed that the average classification accuracy of these two-class tasks was 62.68 ± 5.08%, and the highest classification accuracy was 73.66 ± 6.80%. The study showed that EEG features of O1 and O2 electrodes in the occipital region extracted by HHT were separable for these VMI tasks.

scholarly journals Epileptic Seizures Detection based on Empirical Mode Decomposition and Hilbert-Huang transform of EEG Signal

2015 ◽  
Vol 1 (2) ◽  
pp. 295
Author(s):  
Mokhtar Mohammadi ◽  
Aso M. Darwesh
Keyword(s):  
Empirical Mode Decomposition ◽  
Epileptic Seizure ◽  
Spectral Energy ◽  
Support Vector ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition

The electrical activities of brain fluctuate frequently and can be analyzed using electroencephalogram (EEG) signals. We present a new method for classification of ictal and seizure-free intracranial EEG recordings. The proposed method uses the application of multivariate empirical mode decomposition (MEMD) algorithm combines with the Hilbert transform as the Hilbert-Huang transform (HHT) and analyzing spectral energy of the intrinsic mode function of the signal. EMD uses the characteristics of signals to adaptively decompose them to several intrinsic mode functions (IMFs). Hilbert transforms (HTs) are then used to transform the IMFs into instantaneous frequencies (IFs), to obtain the signals time-frequency-energy distributions. Classification of the EEG signal that is epileptic seizure exists or not has been done using support vector machine. The algorithm was tested in 6 intracranial channels EEG records acquired in 9 patients with refractory epilepsy and validated by the Epilepsy Center of the University Hospital of Freiburg. The experimental results show that the proposed method efficiently detects the presence of epileptic seizure in EEG signals and also showed a reasonable accuracy in detection.

scholarly journals Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine

2020 ◽  
Author(s):  
Lamia Nabil Mahdy ◽  
Kadry Ali Ezzat ◽  
Haytham H. Elmousalami ◽  
Hassan Aboul Ella ◽  
Aboul Ella Hassanien
Keyword(s):  
Early Detection ◽  
Support Vector ◽  
Average Sensitivity ◽  
X Ray ◽  
Infected People ◽  
Proposed Model ◽  
Medical Health Care ◽  
Multi Level ◽  
Sensitivity Specificity

AbstractThe early detection of SARS-CoV-2, the causative agent of (COVID-19) is now a critical task for the clinical practitioners. The COVID-19 spread is announced as pandemic outbreak between people worldwide by WHO since 11/ March/ 2020. In this consequence, it is top critical priority to become aware of the infected people so that prevention procedures can be processed to minimize the COVID-19 spread and to begin early medical health care of those infected persons. In this paper, the deep studying based totally methodology is usually recommended for the detection of COVID-19 infected patients using X-ray images. The help vector gadget classifies the corona affected X-ray images from others through usage of the deep features. The technique is useful for the clinical practitioners for early detection of COVID-19 infected patients. The suggested system of multi-level thresholding plus SVM presented high accuracy in classification of the infected lung with Covid-19. All images were of the same size and stored in JPEG format with 512 * 512 pixels. The average sensitivity, specificity, and accuracy of the lung classification using the proposed model results were 95.76%, 99.7%, and 97.48%, respectively.

A NEW PARAMETRIC FEATURE DESCRIPTOR FOR THE CLASSIFICATION OF EPILEPTIC AND CONTROL EEG RECORDS IN PEDIATRIC POPULATION

2012 ◽  
Vol 22 (02) ◽  
pp. 1250001 ◽  
Author(s):  
MERCEDES CABRERIZO ◽  
MELVIN AYALA ◽  
MOHAMMED GORYAWALA ◽  
PRASANNA JAYAKAR ◽  
MALEK ADJOUADI
Keyword(s):  
Pediatric Population ◽  
Support Vector ◽  
Feature Descriptor ◽  
Scalp Eeg ◽  
Vector Machines ◽  
Epileptic Patients ◽  
Sensitivity Specificity ◽  
And Control

This study evaluates the sensitivity, specificity and accuracy in associating scalp EEG to either control or epileptic patients by means of artificial neural networks (ANNs) and support vector machines (SVMs). A confluence of frequency and temporal parameters are extracted from the EEG to serve as input features to well-configured ANN and SVM networks. Through these classification results, we thus can infer the occurrence of high-risk (epileptic) as well as low risk (control) patients for potential follow up procedures.

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