scholarly journals Seizure Prediction With Spectral Power of EEG Using Cost-Sensitive Support Vector Machines

2010 ◽  
Vol 4 (2) ◽  
Author(s):  
Yun Park ◽  
Theoden Netoff ◽  
Keshab Parhi
Keyword(s):  
Support Vector Machines ◽  
Spectral Power ◽  
Prediction Algorithm ◽  
Patient Specific ◽  
Support Vector ◽  
Seizure Prediction ◽  
Time Space ◽  
Vector Machines ◽  
Out Of Sample ◽  
Eeg Recordings

A patient-specific seizure prediction algorithm is proposed using a classifier to differentiate pre-ictal from inter-ictal EEG signals. The spectral power of EEG processed in four different fashions is used as features: raw, time-differential, space-differential, and time/space-differential EEG. The features are classified using cost-sensitive support vector machines by the double cross-validation methodology. The proposed algorithm has been applied to EEG recordings of 18 patients in the Freiburg EEG database, totaling 80 seizures and 437 h long inter-ictal recordings. Classification with the feature obtained from time/space-differential ECoG demonstrates the performance of 86.25% sensitivity and 0.1281 false positives per hour in out-of-sample testing.

Seizure prediction with spectral power of EEG using cost-sensitive support vector machines

Epilepsia ◽  
2011 ◽  
Vol 52 (10) ◽  
pp. 1761-1770 ◽  
Author(s):  
Yun Park ◽  
Lan Luo ◽  
Keshab K. Parhi ◽  
Theoden Netoff
Keyword(s):  
Support Vector Machines ◽  
Spectral Power ◽  
Support Vector ◽  
Seizure Prediction ◽  
Vector Machines

A Realistic Seizure Prediction Study Based on Multiclass SVM

2017 ◽  
Vol 27 (03) ◽  
pp. 1750006 ◽  
Author(s):  
Bruno Direito ◽  
César A. Teixeira ◽  
Francisco Sales ◽  
Miguel Castelo-Branco ◽  
António Dourado
Keyword(s):  
Statistical Significance ◽  
False Positive Rate ◽  
Patient Specific ◽  
Support Vector ◽  
Seizure Prediction ◽  
Feature Sets ◽  
Scalp Eeg ◽  
Positive Rate ◽  
Eeg Recordings ◽  
Multiclass Svm

A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

Comparing Classification Methods for Longitudinal fMRI Studies

2010 ◽  
Vol 22 (11) ◽  
pp. 2729-2762 ◽  
Author(s):  
Tanya Schmah ◽  
Grigori Yourganov ◽  
Richard S. Zemel ◽  
Geoffrey E. Hinton ◽  
Steven L. Small ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Binary Classification ◽  
Stroke Recovery ◽  
Support Vector ◽  
Linear Quadratic ◽  
Restricted Boltzmann Machines ◽  
K Nearest Neighbors ◽  
Vector Machines ◽  
Out Of Sample ◽  
Classification Tasks

We compare 10 methods of classifying fMRI volumes by applying them to data from a longitudinal study of stroke recovery: adaptive Fisher's linear and quadratic discriminant; gaussian naive Bayes; support vector machines with linear, quadratic, and radial basis function (RBF) kernels; logistic regression; two novel methods based on pairs of restricted Boltzmann machines (RBM); and K-nearest neighbors. All methods were tested on three binary classification tasks, and their out-of-sample classification accuracies are compared. The relative performance of the methods varies considerably across subjects and classification tasks. The best overall performers were adaptive quadratic discriminant, support vector machines with RBF kernels, and generatively trained pairs of RBMs.

scholarly journals PREDICTION OF CONTACT MAPS USING SUPPORT VECTOR MACHINES

2005 ◽  
Vol 14 (05) ◽  
pp. 849-865 ◽  
Author(s):  
YING ZHAO ◽  
GEORGE KARYPIS
Keyword(s):  
Support Vector Machines ◽  
Secondary Structure ◽  
Mutation Analysis ◽  
3D Structure ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Contact Map ◽  
Vector Machines ◽  
Sequence Profiles ◽  
Correlated Mutation

Contact map prediction is of great interest for its application in fold recognition and protein 3D structure determination. In this paper we present a contact-map prediction algorithm that employs Support Vector Machines as the machine learning tool and incorporates various features such as sequence profiles and their conservations, correlated mutation analysis based on various amino acid physicochemical properties, and secondary structure. In addition, we evaluated the effectiveness of the different features on contact map prediction for different fold classes. On average, our predictor achieved a prediction accuracy of 0.224 with an improvement over a random predictor of a factor 11.7, which is better than reported studies. Our study showed that predicted secondary structure features play an important roles for the proteins containing beta-structures. Models based on secondary structure features and correlated mutation analysis features produce different sets of predictions. Our study also suggests that models learned separately for different protein fold families may achieve better performance than a unified model.

Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines

2010 ◽  
Vol 57 (5) ◽  
pp. 1124-1132 ◽  
Author(s):  
Luigi Chisci ◽  
Antonio Mavino ◽  
Guido Perferi ◽  
Marco Sciandrone ◽  
Carmelo Anile ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Real Time ◽  
Epileptic Seizure ◽  
Support Vector ◽  
Seizure Prediction ◽  
Vector Machines ◽  
Epileptic Seizure Prediction ◽  
Ar Models
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