@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}
}
@proceedings {576,
	title = {Automatic diagnosis of ADHD based on nonlinear analysis of actimetry registries},
	volume = {32},
	year = {2011},
	pages = {685-688},
	address = {Prague, Czech Rep.},
	keywords = {ADHD, ADHD automatic diagnosis, Activity/Rest Detection, Attention-Deficit Hyperactivity Disorder, Automatic Diagnosis System, Central Tendency Measure, Feature extraction, Histograms, Indexes, Noise, Pediatrics, Regularity Assessment, Sleep, USA Councils, Wrist, actimetry registries, adolescence, automatic activity/rest detection filter, biomedical measurement, childhood, feature extraction module, medical disorders, medical signal processing, mental health problem, neurophysiology, nonlinear analysis, nonlinear regularity quantification, paediatrics, pathology, signal processing methods},
	author = {Diego Mart{\'\i}n-Mart{\'\i}nez and Pablo Casaseca-de-la-Higuera and Susana Alberola-Lopez and L{\'o}pez-Villalobos, J.A. and Ruiz, F.C. and Jesus Maria Andres-de-Llano and Jose Ramon Garmendia-Leiza and Julio Ardura-Fernández}
}
@inbook {418,
	title = {A statistical-genetic algorithm to select the most significant features in mammograms},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {4673 LNCS},
	year = {2007},
	pages = {189-196},
	abstract = {<p>An automatic classification system into either malignant or benign microcalcification from mammograms is a helpful tool in breast cancer diagnosis. From a set of extracted features, a classifying method using neural networks can provide a probability estimation that can help the radiologist in his diagnosis. With this objective in mind, this paper proposes a feature selection algorithm from a massive number of features based on a statistical distance method in conjunction with a genetic algorithm (GA). The use of a statistical distance as optimality criterion was improved with genetic algorithms for selecting an appropriate subset of features, thus making this algorithm capable of performing feature selection from a massive set of initial features. Additionally, it provides a criterion to select an appropriate number of features to be employed. Experimental work was performed using Generalized Softmax Perceptrons (GSP), trained with a Strict Sense Bayesian cost function for direct probability estimation, as microcalcification classifiers. A Posterior Probability Model Selection (PPMS) algorithm was employed to determine the network complexity. Results showed that this algorithm converges into a subset of features which has a good classification rate and Area Under Curve (AUC) of the Receiver Operating Curve (ROC). {\^A}{\textcopyright} Springer-Verlag Berlin Heidelberg 2007.</p>
},
	keywords = {Breast cancer, Diagnosis, Feature extraction, Genetic algorithms, Mammography, Microcalcification classification, Network complexity, Neural network classifiers, Neural networks, Tumors},
	isbn = {9783540742715},
	issn = {03029743},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-38149142403\&partnerID=40\&md5=ef139db3a0e5d603c4f721316abdcf2c},
	author = {G V Sanchez-Ferrero and J I Arribas}
}
@conference {417,
	title = {A radius and ulna skeletal age assessment system},
	booktitle = {2005 IEEE Workshop on Machine Learning for Signal Processing},
	year = {2005},
	address = {Mystic, CT},
	abstract = {<p>An end to end system to partially automate the TW3 bone age assessment procedure is proposed. The system comprises the detailed analysis of the two more important bones in TW3: the radius and ulna wrist bones. First, a generalization of K-means algorithm is presented to semi-automatically segment the contour of the bones and thus extract up to 89 features describing shapes and textures from bones. Second, a well-founded feature selection criterion based on the statistical properties of data is used in order to properly choose the most relevant features. Third, bone age is estimated with the help of a Generalized Softmax Perceptron (GSP) Neural Network (NN) whose optimal complexity is estimated via the Posterior Probability Model Selection (PPMS) algorithm. We can then predict the different development stages in both radius and ulna, from which we are able to score and estimate the bone age of a patient in years and finally we compare the NN results with those from the pediatrician expert discrepancies. {\^A}{\textcopyright} 2005 IEEE.</p>
},
	keywords = {Algorithms, Bone, Feature extraction, Generalized Softmax Perceptron (GSP), Living systems studies, Neural networks, Probability Model Selection (PPMS), Skeletal age assessment system},
	isbn = {0780395174; 9780780395176},
	doi = {10.1109/MLSP.2005.1532903},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33749052083\&partnerID=40\&md5=eefa29ac09f4efa304b613cf07ab8d10},
	author = {Antonio Trist{\'a}n-Vega and J I Arribas}
}
