EEG Authentication System Based on One- and Multi-Class Machine Learning Classifiers.

In the current Information Age, it is usual to access our personal and professional information, such as bank account data or private documents, in a telematic manner. To ensure the privacy of this information, user authentication systems should be accurately developed. In this work, we focus on biometric authentication, as it depends on the user's inherent characteristics and, therefore, offers personalized authentication systems. Specifically, we propose an electrocardiogram (EEG)-based user authentication system by employing One-Class and Multi-Class Machine Learning classifiers. In this sense, the main novelty of this article is the introduction of Isolation Forest and Local Outlier Factor classifiers as new tools for user authentication and the investigation of their suitability with EEG data. Additionally, we identify the EEG channels and brainwaves with greater contribution to the authentication and compare them with the traditional dimensionality reduction techniques, Principal Component Analysis, and χ2 statistical test. In our final proposal, we elaborate on a hybrid system resistant to random forgery attacks using an Isolation Forest and a Random Forest classifiers, obtaining a final accuracy of 82.3%, a precision of 91.1% and a recall of 75.3%.

Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling.

Deep learning is a recent technology that has shown excellent capabilities for recognition and identification tasks. This study applies these techniques in open cranial vault remodeling surgeries performed to correct craniosynostosis. The objective was to automatically recognize surgical tools in real-time and estimate the surgical phase based on those predictions. For this purpose, we implemented, trained, and tested three algorithms based on previously proposed Convolutional Neural Network architectures (VGG16, MobileNetV2, and InceptionV3) and one new architecture with fewer parameters (CranioNet). A novel 3D Slicer module was specifically developed to implement these networks and recognize surgical tools in real time via video streaming. The training and test data were acquired during a surgical simulation using a 3D printed patient-based realistic phantom of an infant's head. The results showed that CranioNet presents the lowest accuracy for tool recognition (93.4%), while the highest accuracy is achieved by the MobileNetV2 model (99.6%), followed by VGG16 and InceptionV3 (98.8% and 97.2%, respectively). Regarding phase detection, InceptionV3 and VGG16 obtained the best results (94.5% and 94.4%), whereas MobileNetV2 and CranioNet presented worse values (91.1% and 89.8%). Our results prove the feasibility of applying deep learning architectures for real-time tool detection and phase estimation in craniosynostosis surgeries.

Privacy-Preserving Sensor-Based Continuous Authentication and User Profiling: A Review.

Ensuring the confidentiality of private data stored in our technological devices is a fundamental aspect for protecting our personal and professional information. Authentication procedures are among the main methods used to achieve this protection and, typically, are implemented only when accessing the device. Nevertheless, in many occasions it is necessary to carry out user authentication in a continuous manner to guarantee an allowed use of the device while protecting authentication data. In this work, we first review the state of the art of Continuous Authentication (CA), User Profiling (UP), and related biometric databases. Secondly, we summarize the privacy-preserving methods employed to protect the security of sensor-based data used to conduct user authentication, and some practical examples of their utilization. The analysis of the literature of these topics reveals the importance of sensor-based data to protect personal and professional information, as well as the need for exploring a combination of more biometric features with privacy-preserving approaches.

[Effect of sociocultural factors on maternal and perinatal morbidity with or without mortality among adolescents seen in 3 states of the Mexican Republic]. / Influencia de los factores socioculturales en la morbilidad y/o mortalidad materna y perinatal de adolescentes atendidas en tres estados de la República Mexicana.

This study was aimed on comparing the degree of association between social-cultural factors and maternal or perinatal morbidity and/or mortality of the adolescent. A paired case-control study was designed with adolescent in puerperal immediate stage affiliated to the Mexican Institute of Social Security from Tabasco, Tlaxcala and Northern Veracruz, that were adjusted to the selection criteria of the sample, between June of 1998 and February of 1999. Two groups were integrated, cases, with adolescent in puerperal immediate stage affected (with maternal or perinatal morbidity and/or mortality) and controls, with adolescent not affected in puerperal immediate stage. Information concerned to biological and social-cultural risk factors from each subject was obtained applying a validated survey (EFRASEMA 1) and checking their clinical file, whose information was poured in a database (EFRASEMA 2). Interviewers did not know the outcome of the study, which in turn assured the blindness of the information. Once data was obtained, subjects were assigned to each group of study. Matching factors were age, nutritional status, intergenesic interval and previous pregnancy systemic pathology. Proportion of subjects, cases and controls; with or without social-cultural risk factors was determined. The risk of maternal or perinatal morbidity and/or mortality in the exposed subjects was estimated by odds ratio (OR) and the differences inferred through Mantel and Haenszel chi 2 and Fisher's exact tests (confidence intervals alpha = 0.05 and beta = 0.2). There was a sample of 486 subject, 44 were eliminated due to insufficient data. Studied population was integrated finally with 221 cases and 221 paired controls 1: 1. 71.950% of participants were married, 22.62% in free union, 4.98% single and 0.45% separate, average global age was 17.98 +/- 1.39 years. The inferential analysis showed an OR 0.64 (Cornfield 95% confidence limits: 0.40 < OR < 1.03, p = 0.0510600) concerning desired pregnancy in favor to controls. Appropriate reproductive information had an OR = 0.34 (Cornfield 95% confidence limits: 0.21 < OR < 0.54 p = 0.0000014). Ideal cumulated fertility offered an OR 0.62 (Cornfield 95% confidence limits: 0.39 < OR < 0.98, p = 0.0298500). These results show an association between the social-cultural factors and the presence of maternal or perinatal morbidity and/or mortality in the studied adolescents. Desired pregnancy, appropriate reproductive information and ideal cumulated fertility are protection factors to maternal or perinatal morbidity and/or mortality.