Feature extraction and wall motion classification of 2D stress echocardiography with relevance vector machines

2011 ◽  
Author(s):  
Kiryl Chykeyuk ◽  
David A. Clifton ◽  
J. Alison Noble
Keyword(s):  
Feature Extraction ◽  
Stress Echocardiography ◽  
Wall Motion ◽  
Motion Classification ◽  
Relevance Vector Machines ◽  
Vector Machines

scholarly journals Superpixel-Based Minimum Noise Fraction Feature Extraction for Classification of Hyperspectral Images

2020 ◽  
Vol 37 (5) ◽  
pp. 812-822
Author(s):  
Behnam Asghari Beirami ◽  
Mehdi Mokhtarzade
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Hyperspectral Images ◽  
Support Vector ◽  
Minimum Noise Fraction ◽  
Vector Machines ◽  
Noise Covariance ◽  
Noise Fraction ◽  
Minimum Noise

In this paper, a novel feature extraction technique called SuperMNF is proposed, which is an extension of the minimum noise fraction (MNF) transformation. In SuperMNF, each superpixel has its own transformation matrix and MNF transformation is performed on each superpixel individually. The basic idea behind the SuperMNF is that each superpixel contains its specific signal and noise covariance matrices which are different from the adjacent superpixels. The extracted features, owning spatial-spectral content and provided in the lower dimension, are classified by maximum likelihood classifier and support vector machines. Experiments that are conducted on two real hyperspectral images, named Indian Pines and Pavia University, demonstrate the efficiency of SuperMNF since it yielded more promising results than some other feature extraction methods (MNF, PCA, SuperPCA, KPCA, and MMP).

Classification of data from electronic nose using relevance vector machines

2009 ◽  
Vol 140 (1) ◽  
pp. 143-148 ◽  
Author(s):  
Xiaodong Wang ◽  
Meiying Ye ◽  
C.J. Duanmu
Keyword(s):  
Electronic Nose ◽  
Relevance Vector Machines ◽  
Vector Machines

scholarly journals Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2403
Author(s):  
Jakub Browarczyk ◽  
Adam Kurowski ◽  
Bozena Kostek
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mental States ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Vector Machines

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

scholarly journals Feature extraction from terahertz pulses for classification of RNA data via support vector machines

2006 ◽  
Author(s):  
Xiaoxia Yin ◽  
Brian W.-H. Ng ◽  
Bernd Fischer ◽  
Bradley Ferguson ◽  
Samuel P. Mickan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Support Vector Machines ◽  
Support Vector ◽  
Terahertz Pulses ◽  
Vector Machines

FEATURE EXTRACTION AND CLASSIFICATION OF FETAL HEART RATE USING WAVELET ANALYSIS AND SUPPORT VECTOR MACHINES

2006 ◽  
Vol 15 (03) ◽  
pp. 411-432 ◽  
Author(s):  
GEORGE GEORGOULAS ◽  
CHRYSOSTOMOS STYLIOS ◽  
PETER GROUMPOS
Keyword(s):  
Heart Rate ◽  
Feature Extraction ◽  
Support Vector Machines ◽  
Fetal Heart Rate ◽  
Umbilical Artery ◽  
Fetal Heart ◽  
Support Vector ◽  
Ph Values ◽  
Vector Machines

Since the fetus is not available for direct observations, only indirect information can guide the obstetrician in charge. Electronic Fetal Monitoring (EFM) is widely used for assessing fetal well being. EFM involves detection of the Fetal Heart Rate (FHR) signal and the Uterine Activity (UA) signal. The most serious fetal incident is the hypoxic injury leading to cerebral palsy or even death, which is a condition that must be predicted and avoided. This research work proposes a new integrated method for feature extraction and classification of the FHR signal able to associate FHR with umbilical artery pH values at delivery. The proposed method introduces the use of the Discrete Wavelet Transform (DWT) to extract time-scale dependent features of the FHR signal and the use of Support Vector Machines (SVMs) for the categorization. The proposed methodology is tested on a data set of intrapartum recordings were the FHR categories are associated with umbilical artery pH values, This proposed approach achieved high overall classification performance proving its merits.

IMPLEMENTATION OF SUPPORT VECTOR MACHINES FOR CLASSIFICATION OF CLINICAL DATASETS

2010 ◽  
Vol 07 (04) ◽  
pp. 347-356
Author(s):  
E. SIVASANKAR ◽  
R. S. RAJESH
Keyword(s):  
Feature Extraction ◽  
Dimensional Space ◽  
Principal Component ◽  
Kernel Functions ◽  
Support Vector ◽  
Optimal Hyperplane ◽  
Higher Dimensional ◽  
Vector Machines ◽  
Map Data

In this paper, Principal Component Analysis is used for feature extraction, and a statistical learning based Support Vector Machine is designed for functional classification of clinical data. Appendicitis data collected from BHEL Hospital, Trichy is taken and classified under three classes. Feature extraction transforms the data in the high-dimensional space to a space of fewer dimensions. The classification is done by constructing an optimal hyperplane that separates the members from the nonmembers of the class. For linearly nonseparable data, Kernel functions are used to map data to a higher dimensional space and there the optimal hyperplane is found. This paper works with different SVMs based on radial basis and polynomial kernels, and their performances are compared.

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