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

2018 ◽  
Author(s):  
Xiashuang Wang ◽  
Guanghong Gong ◽  
Ni Li
Keyword(s):  
Classification Accuracy ◽  
Classification Scheme ◽  
Normal Subjects ◽  
Gradient Boosting ◽  
Support Vector ◽  
Grid Search ◽  
Automated Recognition ◽  
Time Frequency ◽  
Eeg Data ◽  
Gradient Boosting Machine

Automatic recognition methods for non-stationary EEG data collected from EEG sensors play an essential role in neurological detection. The integrative approaches proposed in this study consists of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-level classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets were adopted to decompose the EEG data into five time-frequency sub-bands, whose statistical features were computed and used as classification features. The grid search optimizer was 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 support vector machine and a random forest classifier constructed according to previous descriptions. Multiple-index were used to evaluate the Symlet wavelet transform-gradient boosting machine-grid search optimizer classification scheme, which provided better classification accuracy and detection effectiveness than has recently reported in other work on three-level classification of EEG data.

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.

Using Stratified Sample and Grid Search to Improve Disease Prediction Accuracy of SVM

2013 ◽  
Vol 295-298 ◽  
pp. 644-647 ◽  
Author(s):  
Yu Kai Yao ◽  
Hong Mei Cui ◽  
Ming Wei Len ◽  
Xiao Yun Chen
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Disease Prediction ◽  
Data Mining Algorithm ◽  
Grid Search ◽  
Mining Algorithm ◽  
Stratified Sample

SVM (Support Vector Machine) is a powerful data mining algorithm, and is mainly used to finish classification or regression tasks. In this literature, SVM is used to conduct disease prediction. We focus on integrating with stratified sample and grid search technology to improve the classification accuracy of SVM, thus, we propose an improved algorithm named SGSVM: Stratified sample and Grid search based SVM. To testify the performance of SGSVM, heart-disease data from UCI are used in our experiment, and the results show SGSVM has obvious improvement in classification accuracy, and this is very valuable especially in disease prediction.

scholarly journals Classification of Emotional States Inparkinson’s Disease Patients Using Time,Frequency and Time-Frequency Analysis

2021 ◽  
Author(s):  
Rejith K.N ◽  
Kamalraj Subramaniam ◽  
Ayyem Pillai Vasudevan Pillai ◽  
Roshini T V ◽  
Renjith V. Ravi ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Signal Frequency ◽  
Spectral Energy ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Frequency ◽  
Energy Entropy ◽  
Teager Energy

Abstract In this work, PD patients and healthy individuals were categorized with machine-learning algorithms. EEG signals associated with six different emotions, (Happiness(E1), Sadness(E2), Fear(E3), Anger(E4), Surprise,(E5) and disgust(E6)) were used for the study. EEG data were collected from 20 PD patients and 20 normal controls using multimodal stimuli. Different features were used to categorize emotional data. Emotional recognition in Parkinson’s disease (PD) has been investigated in three domains namely, time, frequency and time frequency using Entropy, Energy-Entropy and Teager Energy-Entropy features. Three classifiers namely, K-Nearest Neighbor Algorithm, Support Vector Machine and Probabilistic Neural Network were used to observethe classification results. Emotional EEG stimuli such as anger, surprise, happiness, sadness, fear, and disgust were used to categorize PD patients and healthy controls (HC). For each EEG signal, frequency features corresponding to alpha, beta and gamma bands were obtained for nine feature extraction methods (Entropy, Energy Entropy, Teager Energy Entropy, Spectral Entropy, Spectral Energy-Entropy, Spectral Teager Energy-Entropy, STFT Entropy, STFT Energy-Entropy and STFT Teager Energy-Entropy). From the analysis, it is observed that the entropy feature in frequency domain performs evenly well (above 80 %) for all six emotions with KNN. Classification results shows that using the selected energy entropy combination feature in frequency domain provides highest accuracy for all emotions except E1 and E2 for KNN and SVM classifier, whereas other features give accuracy values of above 60% for most emotions.It is also observed that emotion E1 gives above 90 % classification accuracy for all classifiers in time domain.In frequency domain also, emotion E1 gives above 90% classification accuracy using PNN classifier.

Practical Artifact Removal Brain-Computer Interface System

2014 ◽  
pp. 265-277
Author(s):  
Wei-Yen Hsu
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Artifact Removal ◽  
Eeg Data ◽  
Interface System ◽  
Electroencephalogram Eeg ◽  
Bci System

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management

2022 ◽  
pp. 80-127
Author(s):  
Viswanathan Rajagopalan ◽  
Houwei Cao
Keyword(s):  
Artificial Intelligence ◽  
Disease Management ◽  
Heart Attack ◽  
Congenital Heart ◽  
The United States ◽  
Disease Classification ◽  
Gradient Boosting ◽  
Support Vector ◽  
Gradient Boosting Machine ◽  
Infarction Heart

Despite significant advancements in diagnosis and disease management, cardiovascular (CV) disorders remain the No. 1 killer both in the United States and across the world, and innovative and transformative technologies such as artificial intelligence (AI) are increasingly employed in CV medicine. In this chapter, the authors introduce different AI and machine learning (ML) tools including support vector machine (SVM), gradient boosting machine (GBM), and deep learning models (DL), and their applicability to advance CV diagnosis and disease classification, and risk prediction and patient management. The applications include, but are not limited to, electrocardiogram, imaging, genomics, and drug research in different CV pathologies such as myocardial infarction (heart attack), heart failure, congenital heart disease, arrhythmias, valvular abnormalities, etc.

Learning Advanced Brain Computer Interface Technology

2019 ◽  
Vol 15 (3) ◽  
pp. 14-27
Author(s):  
Wang Tao ◽  
Wu Linyan ◽  
Li Yanping ◽  
Gao Nuo ◽  
Zhang Weiran
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Support Vector ◽  
Wavelet Packet Analysis ◽  
Accuracy Rate ◽  
Desirable Outcome ◽  
Eeg Data ◽  
System Data

Feature extraction is an important step in electroencephalogram (EEG) processing of motor imagery, and the feature extraction of EEG directly affects the final classification results. Through the analysis of various feature extraction methods, this article finally selects Common Spatial Patterns (CSP) and wavelet packet analysis (WPA) to extract the feature and uses Support Vector Machine (SVM) to classify and compare these extracted features. For the EEG data provided by GRAZ University, the accuracy rate of feature extraction using CSP algorithm is 85.5%, and the accuracy rate of feature extraction using wavelet packet analysis is 92%. Then this paper analyzes the EEG data collected by Emotiv epoc+ system. The classification accuracy of wavelet packet extracted features can still be maintained at more than 80%, while the classification accuracy of CSP extracted feature is decreased obviously. Experimental results show that the method of wavelet packet analysis towards competition data and Emotiv epoc+ system data can both get a desirable outcome.

EEG characteristic investigation of the sixth-finger motor imagery and optimal channel selection for classification

2022 ◽  
Author(s):  
Yuan Liu ◽  
Zhuang Wang ◽  
Shuaifei Huang ◽  
Wenjie Wang ◽  
Dong Ming
Keyword(s):  
Frontal Lobe ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Motor Area ◽  
Support Vector ◽  
Human Hand ◽  
New Paradigm ◽  
Time Frequency ◽  
Spatial Features ◽  
Robotic Devices

Abstract Objective. Supernumerary Robotic Limbs (SRL) are body augmentation robotic devices by adding extra limbs or fingers to the human body different from the traditional wearable robotic devices such as prosthesis and exoskeleton. We proposed a novel MI (Motor imagery)-based BCI paradigm based on the sixth-finger which imagines controlling the extra finger movements. The goal of this work is to investigate the EEG characteristics and the application potential of MI-based BCI systems based on the new imagination paradigm (the sixth finger MI). Approach. 14 subjects participated in the experiment involving the sixth finger MI tasks and rest state. Event-related spectral perturbation (ERSP) was adopted to analyse EEG spatial features and key-channel time-frequency features. Common spatial patterns (CSP) were used for feature extraction and classification was implemented by support vector machine (SVM). A genetic algorithm (GA) was used to select combinations of EEG channels that maximized classification accuracy and verified EEG patterns based on the sixth finger MI. And we conducted a longitudinal 4-week EEG control experiment based on the new paradigm. Main results. ERD (event-related desynchronization) was found in the supplementary motor area (SMA) and primary motor area (M1) with a faint contralateral dominance. Unlike traditional MI based on the human hand, ERD was also found in frontal lobe. GA results showed that the distribution of the optimal 8-channel is similar to EEG topographical distributions, nearing parietal and frontal lobe. And the classification accuracy based on the optimal 8-channel (the highest accuracy of 80% and mean accuracy of 70%) was significantly better than that based on the random 8-channel (p<0.01). Significance. This work provided a new paradigm for MI-based MI system and verified its feasibility, widened the control bandwidth of the BCI system.

scholarly journals Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation

2019 ◽  
Vol 8 (2) ◽  
pp. 97 ◽  
Author(s):  
Mustafa Ustuner ◽  
Fusun Balik Sanli
Keyword(s):  
Classification Accuracy ◽  
Learning Algorithm ◽  
Test Site ◽  
Gradient Boosting ◽  
Model Based ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
Multi Temporal ◽  
Decomposition Models ◽  
Crop Classification

In terms of providing various scattering mechanisms, polarimetric target decompositions provide certain benefits for the interpretation of PolSAR images. This paper tested the capabilities of different polarimetric target decompositions in crop classification, while using a recently launched ensemble learning algorithm—namely Light Gradient Boosting Machine (LightGBM). For the classification of different crops (maize, potato, wheat, sunflower, and alfalfa) in the test site, multi-temporal polarimetric C-band RADARSAT-2 images were acquired over an agricultural area near Konya, Turkey. Four different decomposition models (Cloude–Pottier, Freeman–Durden, Van Zyl, and Yamaguchi) were employed to evaluate polarimetric target decomposition for crop classification. Besides the polarimetric target decomposed parameters, the original polarimetric features (linear backscatter coefficients, coherency, and covariance matrices) were also incorporated for crop classification. The experimental results demonstrated that polarimetric target decompositions, with the exception of Cloude–Pottier, were found to be superior to the original features in terms of overall classification accuracy. The highest classification accuracy (92.07%) was achieved by Yamaguchi, whereas the lowest (75.99%) was achieved by the covariance matrix. Model-based decompositions achieved higher performance with respect to eigenvector-based decompositions in terms of class-based accuracies. Furthermore, the results emphasize the added benefits of model-based decompositions for crop classification using PolSAR data.

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