Cross-correlation aided support vector machine classifier for classification of EEG signals

2009 ◽  
Vol 36 (2) ◽  
pp. 1329-1336 ◽  
Author(s):  
Suryannarayana Chandaka ◽  
Amitava Chatterjee ◽  
Sugata Munshi
Keyword(s):  
Support Vector Machine ◽  
Cross Correlation ◽  
Support Vector Machine Classifier ◽  
Support Vector ◽  
Eeg Signals

scholarly journals Classification of Sleep Apnea Based on Sub-Band Decomposition of EEG Signals

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1571
Author(s):  
Rajeswari Jayaraj ◽  
Jagannath Mohan
Keyword(s):  
Support Vector Machine ◽  
Sleep Apnea ◽  
Support Vector Machine Classifier ◽  
Wavelet Packet ◽  
Brain Perfusion ◽  
Support Vector ◽  
Eeg Signals ◽  
Data Format ◽  
Electroencephalogram Eeg

To classify between normal and sleep apnea subjects based on sub-band decomposition of electroencephalogram (EEG) signals. This study comprised 159 subjects obtained from the ISRUC (Institute of System and Robotics—University of Coimbra), Sleep-EDF (European Data Format), and CAP (Cyclic Alternating Pattern) Sleep database, which consists of normal and sleep apnea subjects. The wavelet packet decomposition method was incorporated to categorize the EEG signals into five frequency bands, namely, alpha, beta, delta, gamma, and theta. Entropy and energy (non-linear) for all bands was calculated and as a result, 10 features were obtained for each EEG signal. The ratio of EEG bands included four parameters, including heart rate, brain perfusion, neural activity, and synchronization. In this study, a support vector machine with kernels and random forest classifiers was used for classification. The performance measures demonstrated that the improved results were obtained from the support vector machine classifier with a kernel polynomial order 2. The accuracy (90%), sensitivity (100%), and specificity (83%) with 14 features were estimated using the data obtained from ISRUC database. The proposed study is feasible and seems to be accurate in classifying the subjects with sleep apnea based on the extracted features from EEG signals using a support vector machine classifier.

MULTI-CLUSTER SUPPORT VECTOR MACHINE CLASSIFIER FOR THE CLASSIFICATION OF SUSPICIOUS AREAS IN DIGITAL MAMMOGRAMS

2011 ◽  
Vol 10 (04) ◽  
pp. 481-494 ◽  
Author(s):  
PETER MC LEOD ◽  
BRIJESH VERMA
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Screening Mammography ◽  
Support Vector ◽  
Svm Classifier ◽  
Anova Analysis ◽  
Novel Technique ◽  
Digital Database ◽  
Benchmark Database

This paper presents a novel technique for the classification of suspicious areas in digital mammograms. The proposed technique is based on clustering of input data into numerous clusters and amalgamating them with a Support Vector Machine (SVM) classifier. The technique is called multi-cluster support vector machine (MCSVM) and is designed to provide a fast converging technique with good generalization abilities leading to an improved classification as a benign or malignant class. The proposed MCSVM technique has been evaluated on data from the Digital Database of Screening Mammography (DDSM) benchmark database. The experimental results showed that the proposed MCSVM classifier achieves better results than standard SVM. A paired t-test and Anova analysis showed that the results are statistically significant.

A MODIFIED LEAST SQUARES SUPPORT VECTOR MACHINE CLASSIFIER WITH APPLICATION TO CREDIT RISK ANALYSIS

2009 ◽  
Vol 08 (04) ◽  
pp. 697-710 ◽  
Author(s):  
LEAN YU ◽  
SHOUYANG WANG ◽  
JIE CAO
Keyword(s):  
Support Vector Machine ◽  
Risk Analysis ◽  
Credit Risk ◽  
Least Squares ◽  
Support Vector Machine Classifier ◽  
Support Vector ◽  
Original Form ◽  
Simple Modification ◽  
Credit Risk Analysis

In this paper, a modified least squares support vector machine classifier, called the C-variable least squares support vector machine (C-VLSSVM) classifier, is proposed for credit risk analysis. The main idea of the proposed classifier is based on the prior knowledge that different classes may have different importance for modeling and more weight should be given to classes having more importance. The C-VLSSVM classifier can be obtained by a simple modification of the regularization parameter, based on the least squares support vector machine (LSSVM) classifier, whereby more weight is given to errors in classification of important classes, than to errors in classification of unimportant classes, while keeping the regularized terms in their original form. For illustration purpose, two real-world credit data sets are used to verify the effectiveness of the C-VLSSVM classifier. Experimental results obtained reveal that the proposed C-VLSSVM classifier can produce promising classification results in credit risk analysis, relative to other classifiers listed in this study.

scholarly journals Automated classification of bacterial particles in flow by multiangle scatter measurement and support vector machine classifier

2008 ◽  
Vol 73A (4) ◽  
pp. 369-379 ◽  
Author(s):  
Bartek Rajwa ◽  
Murugesan Venkatapathi ◽  
Kathy Ragheb ◽  
Padmapriya P. Banada ◽  
E. Daniel Hirleman ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Support Vector ◽  
Automated Classification
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