@article {7460246, title = {A computer-aided diagnosis system with EEG based on the P3b wave during an auditory odd-ball task in schizophrenia}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {PP}, number = {99}, year = {2016}, pages = {1-1}, abstract = {Objective: To design a Computer-aided diagnosis (CAD) system using an optimized methodology over the P3b wave in order to objectively and accurately discriminate between healthy controls (HC) and schizophrenic subjects (SZ). Methods: We train, test, analyze, and compare various machine learning classification approaches optimized in terms of the correct classification rate (CCR), the degenerated Youden{\textquoteright}s index (DYI) and the area under the receiver operating curve (AUC). CAD system comprises five stages: electroencephalography (EEG) preprocessing, feature extraction, seven electrode groupings, discriminant feature selection, and binary classification. Results: With two optimal combinations of electrode grouping, filtering, feature selection algorithm, and classification machine, we get either a mean CCR = 93.42\%, specificity = 0.9673, sensitivity = 0.8727, DYI = 0.9188, and AUC = 0.9567 (total-15 Hz-J5-MLP), or a mean CCR = 92.23\%, specificity = 0.9499, sensitivity = 0.8838, DYI = 0.9162, and AUC = 0.9807 (right hemisphere-35 Hz-J5-SVM), which to our knowledge are higher than those available to date. Conclusions: We have verified that a more restrictive low-pass filtering achieves higher CCR as compared to others at higher frequencies in the P3b wave. In addition, results validate previous hypothesis about the importance of the parietal-temporal region, associated with memory processing, allowing us to identify powerful {feature,electrode} pairs in the diagnosis of schizophrenia, achieving higher CCR and AUC in classification of both right and left Hemispheres, and parietal-temporal EEG signals, like, for instance, the {PSE, P4} pair (J5 and mutual information feature selection). Significance: Diagnosis of schizophrenia is made thoroughly by psychiatrists but as any human-based decision that has a subjective component. This CAD system provides the human expert with an objective complimentary measure to help him in diagnosing schizophrenia.}, keywords = {Computer aided diagnosis, Design automation, Electrodes, Electroencephalography, Feature extraction, Indexes, Sensitivity}, issn = {0018-9294}, doi = {10.1109/TBME.2016.2558824}, url = {https://ieeexplore.ieee.org/abstract/document/7460246}, author = {L. Santos-Mayo and Luis Miguel San-Jose-Revuelta and J I Arribas} } @article {san2008efficient, title = {Efficient tracking of MR tensor fields using a multilayer neural network}, journal = {IAENG International Journal of Computer Science}, volume = {35}, number = {1}, year = {2008}, pages = {129{\textendash}139}, author = {Luis Miguel San-Jose-Revuelta and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @proceedings {san2008ga, title = {A GA-based Approach for Parameter Estimation in DT-MRI Tracking Algorithms}, volume = {1}, year = {2008}, author = {Luis Miguel San-Jose-Revuelta and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @conference {san2007neural, title = {Neural Network-Assisted Fiber Tracking of Synthetic and White Matter DT-MR Images.}, booktitle = {World Congress on Engineering}, year = {2007}, pages = {618{\textendash}623}, author = {Luis Miguel San-Jose-Revuelta and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @conference {san2007new, title = {A new proposal for 3D fiber tracking in synthetic diffusion tensor magnetic resonance images}, booktitle = {Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on}, year = {2007}, pages = {1{\textendash}4}, publisher = {IEEE}, organization = {IEEE}, author = {Luis Miguel San-Jose-Revuelta and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @proceedings {san2006new, title = {A new method for fiber tractography in diffusion tensor magnetic resonance images}, volume = {6}, year = {2006}, pages = {380{\textendash}385}, author = {Luis Miguel San-Jose-Revuelta and Marcos Martin-Fernandez and Carlos Alberola-Lopez} }