An EEG-based perceptual function integration network for application to drowsy driving

© 2015 Elsevier B.V. All rights reserved. Drowsy driving is among the most critical causes of fatal crashes. Thus, the development of an effective algorithm for detecting a driver's cognitive state demands immediate attention. For decades, studies have observed clear evidence using electroencephalography that the brain's rhythmic activities fluctuate from alertness to drowsiness. Recognition of this physiological signal is the major consideration of neural engineering for designing a feasible countermeasure. This study proposed a perceptual function integration system which used spectral features from multiple independent brain sources for application to recognize the driver's vigilance state. The analysis of brain spectral dynamics demonstrated physiological evidenced that the activities of the multiple cortical sources were highly related to the changes of the vigilance state. The system performances showed a robust and improved accuracy as much as 88% higher than any of results performed by a single-source approach

Prevention of cervical cancer among female undergraduates in two universities in south-western Nigeria

Background: Cervical cancer (CC) is the leading cause of death from cancer among women in developing countries.Objectives: The study assessed the effects of an educational package on the knowledge of female undergraduates (FUs) on CC and its prevention using the Pap smear.Study Design: A quasi experimental design was adopted. Two universities in South west Nigeria was used. Forty subjects who were sexually active were recruited from each university. Instruments used were an educational package and a semi structured questionnaire. The experimental subjects were given access to free Pap smear as a preventive measure. Data collected were analyzed using descriptive statistics and test. Ethical clearance was taken from the institutions while informed consent was taken from each subject.Results: The results showed that 10% of experimental and 17.5% of the control subjects had good knowledge of CC and its prevention at pre-intervention. At post intervention, 92.5% of the experimental and 35% of the control group had good knowledge. During the intervention, 42.5% of experimental had Pap's Smear. Of these subjects that had Pap smear, 47% had abnormal results that required cytology and further investigation. At p= 0.001, there was a significant difference in the mean scores of both groups.Conclusion: The introduction of a health education package and provision of Pap smear significantly improved the knowledge of FUs on CC and their uptake of Pap smear. It is recommended that health education on CC and prevention using HPV vaccines and Pap smear be given to University students.Keywords: Cervical Cancer, Educational Package, Pap Smear, Female UndergraduatesTrop J Obstet Gynaecol, 30 (1), April 201

Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels

© 2014 Huang, Lin, Ko, Liu, Su and Lin. Sleep quality is important, especially given the considerable number of sleep-related pathologies. The distribution of sleep stages is a highly effective and objective way of quantifying sleep quality. As a standard multi-channel recording used in the study of sleep, polysomnography (PSG) is a widely used diagnostic scheme in sleep medicine. However, the standard process of sleep clinical test, including PSG recording and manual scoring, is complex, uncomfortable, and time-consuming. This process is difficult to implement when taking the whole PSG measurements at home for general healthcare purposes. This work presents a novel sleep stage classification system, based on features from the two forehead EEG channels FP1 and FP2. By recording EEG from forehead, where there is no hair, the proposed system can monitor physiological changes during sleep in a more practical way than previous systems. Through a headband or self-adhesive technology, the necessary sensors can be applied easily by users at home. Analysis results demonstrate that classification performance of the proposed system overcomes the individual differences between different participants in terms of automatically classifying sleep stages. Additionally, the proposed sleep stage classification system can identify kernel sleep features extracted from forehead EEG, which are closely related with sleep clinician's expert knowledge. Moreover, forehead EEG features are classified into five sleep stages by using the relevance vector machine. In a leave-one-subject-out cross validation analysis, we found our system to correctly classify five sleep stages at an average accuracy of 76.7 ± 4.0 (SD) % [average kappa 0.68 ± 0.06 (SD)]. Importantly, the proposed sleep stage classification system using forehead EEG features is a viable alternative for measuring EEG signals at home easily and conveniently to evaluate sleep quality reliably, ultimately improving public healthcare

Developing an EEG-based on-line closed-loop lapse detection and mitigation system

© 2014 Wang, Huang, Wei, Huang, Ko, Lin, Cheng and Jung. In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments

No evidence for a common blood microbiome based on a population study of 9,770 healthy humans

Human blood is conventionally considered sterile but recent studies suggest the presence of a blood microbiome in healthy individuals. Here we characterized the DNA signatures of microbes in the blood of 9,770 healthy individuals using sequencing data from multiple cohorts. After filtering for contaminants, we identified 117 microbial species in blood, some of which had DNA signatures of microbial replication. They were primarily commensals associated with the gut (n = 40), mouth (n = 32) and genitourinary tract (n = 18), and were distinct from pathogens detected in hospital blood cultures. No species were detected in 84% of individuals, while the remainder only had a median of one species. Less than 5% of individuals shared the same species, no co-occurrence patterns between different species were observed and no associations between host phenotypes and microbes were found. Overall, these results do not support the hypothesis of a consistent core microbiome endogenous to human blood. Rather, our findings support the transient and sporadic translocation of commensal microbes from other body sites into the bloodstream

Statistical Hadronization of Supercooled Quark-Gluon Plasma

The fast simultaneous hadronization and chemical freeze out of supercooled quark-gluon plasma, created in relativistic heavy ion collisions, leads to the re-heating of the expanding matter and to the change in a collective flow profile. We use the assumption of statistical nature of the hadronization process, and study quantitatively the freeze out in the framework of hydrodynamical Bjorken model with different quark-gluon plasma equations of state.Comment: 7 pages, 3 figure

Eyeglasses frame selection based on oval face shape using convolutional neural network

Deep Learning is one part of the Machine Learning which is now widely discussed by researchers. One use of Deep Learning is on the case of image classification. One method that can be used is Convolutional Neural Network. In this case, by using Convolutional Neural Network, it will identify a person’s face shape is oval or not. This method will be implemented into Android OS and using Python Language to help in processing the data that retrieve and observe from search engine Google. Viola-Jones Algorithm is useful to analyze the existence of the faces in the image processing. The output that will be accomplished is an application that can give user a choice of frames of glasses based on face shapes. Through this application user can try a variety of frames with practice rather than having to try it in physical form one by one. © 2019, ICIC International. All rights reserved

Portuguese validation of the Bergen Facebook Addiction Scale: an Empirical Study

Previous research on Social Networking Sites (SNSs) addiction have suggest the need to improve assessment of this behavioral addiction. The present study aimed at validating a Portuguese version of the Bergen Facebook Addiction Scale (BFAS), a widely used instrument to assess addiction to Facebook. A study was conducted in a sample of 509 Portuguese adolescent using an online survey. The psychometric properties (construct validity, criterion validity, and reliability) of the Portuguese BFAS was scrutinized. The results from the psychometric analyses suggested that the new validated instrument had excellent psychometric properties. The CFA confirmed the original one-factor solution of the BFAS and criterion validity was warranted. The reliability of the BFAS was supported by satisfactory levels of internal consistency as measured by the Cronbach’s alpha (α = .83), composite reliability (CR = .82), and factor determinacy (FD = .91). Overall, the results provided empirical support for the validity and reliability of the Portuguese BFAS. Moreover, the results were highly comparable with the findings of the original development study of the BFAS and cross-cultural support for the scale was obtained

Mobile Application for Asthma Prediction using Fuzzy-Certainty Factor Expert System

Asthma is a chronic illness that sporadically affects the ability of the person who has it to breathe. It is reported that out of the 417,918 global deaths attributed to asthma in 2016, and the majority occurred in low- and lower-middle-income countries. Asthma sufferers in these countries often do not realize that they have asthma and leave the symptoms untreated because they have limited access to affordable and quality healthcare. We propose a methodology to address this issue by bringing the medical experts closer to the poorer segment of the population via a mobile application. The application uses a rule-based algorithm based on a fuzzy-certainty factor model to manage uncertainties that are present when collecting data from the experts and users. The result of the experiment shows that this technique manages to accurately predict the occurrence of the disease and identify the type of the disease with 96.7% accuracy