Digital filter implementation over FPGA platform with LINUX OS

AbstractThe embedded processors on FPGA's are a good tool to specific propose works. In this work we present how the FPGA is used to apply a Sobel filter to a set of images, also the step needed to set-up the entire system is described. An embedded processor, with a Linux distribution implemented is used to run a special compilation of C filter program, the filter is compared with the results obtained with a PC running the same filter, in the embedded system all the process runs in the FPGA and the exit file can be accessed by ftp or http server embedded into the Linux system

Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013–2014

Oral health represents an essential component in the quality of life of people, being a determinant factor in general health since it may affect the risk of suffering other conditions, such as chronic diseases. Oral diseases have become one of the main public health problems, where dental caries is the condition that most affects oral health worldwide, occurring in about 90% of the global population. This condition has been considered a challenge because of its high prevalence, besides being a chronic but preventable disease which can be caused depending on the consumption of certain nutritional elements interacting simultaneously with different factors, such as socioeconomic factors. Based on this problem, an analysis of a set of 189 dietary and demographic determinants is performed in this work, in order to find the relationship between these factors and the oral situation of a set of subjects. The oral situation refers to the presence and absence/restorations of caries. The methodology is performed constructing a dense artificial neural network (ANN), as a computer-aided diagnosis tool, looking for a generalized model that allows for classifying subjects. As validation, the classification model was evaluated through a statistical analysis based on a cross validation, calculating the accuracy, loss function, receiving operating characteristic (ROC) curve and area under the curve (AUC) parameters. The results obtained were statistically significant, obtaining an accuracy ≃ 0.69 and AUC values of 0.69 and 0.75. Based on these results, it is possible to conclude that the classification model developed through the deep ANN is able to classify subjects with absence of caries from subjects with presence or restorations with high accuracy, according to their demographic and dietary factors

Univariate Analysis of Short-Chain Fatty Acids Related to Sudden Infant Death Syndrome

Sudden infant death syndrome (SIDS) is defined as the death of a child under one year of age, during sleep, without apparent cause, after exhaustive investigation, so it is a diagnosis of exclusion. SIDS is the principal cause of death in industrialized countries. Inborn errors of metabolism (IEM) have been related to SIDS. These errors are a group of conditions characterized by the accumulation of toxic substances usually produced by an enzyme defect and there are thousands of them and included are the disorders of the β-oxidation cycle, similarly to what can affect the metabolism of different types of fatty acid chain (within these, short chain fatty acids (SCFAs)). In this work, an analysis of postmortem SCFAs profiles of children who died due to SIDS is proposed. Initially, a set of features containing SCFAs information, obtained from the NIH Common Fund’s National Metabolomics Data Repository (NMDR) is submitted to an univariate analysis, developing a model based on the relationship between each feature and the binary output (death due to SIDS or not), obtaining 11 univariate models. Then, each model is validated, calculating their receiver operating characteristic curve (ROC curve) and area under the ROC curve (AUC) value. For those features whose models presented an AUC value higher than 0.650, a new multivariate model is constructed, in order to validate its behavior in comparison to the univariate models. In addition, a comparison between this multivariate model and a model developed based on the whole set of features is finally performed. From the results, it can be observed that each SCFA which comprises of the SFCAs profile, has a relationship with SIDS and could help in risk identification

Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods

The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19

Intracranial pressure monitoring in patients with acute brain injury in the intensive care unit (SYNAPSE-ICU): an international, prospective observational cohort study

Background: The indications for intracranial pressure (ICP) monitoring in patients with acute brain injury and the effects of ICP on patients’ outcomes are uncertain. The aims of this study were to describe current ICP monitoring practises for patients with acute brain injury at centres around the world and to assess variations in indications for ICP monitoring and interventions, and their association with long-term patient outcomes. Methods: We did a prospective, observational cohort study at 146 intensive care units (ICUs) in 42 countries. We assessed for eligibility all patients aged 18 years or older who were admitted to the ICU with either acute brain injury due to primary haemorrhagic stroke (including intracranial haemorrhage or subarachnoid haemorrhage) or traumatic brain injury. We included patients with altered levels of consciousness at ICU admission or within the first 48 h after the brain injury, as defined by the Glasgow Coma Scale (GCS) eye response score of 1 (no eye opening) and a GCS motor response score of at least 5 (not obeying commands). Patients not admitted to the ICU or with other forms of acute brain injury were excluded from the study. Between-centre differences in use of ICP monitoring were quantified by using the median odds ratio (MOR). We used the therapy intensity level (TIL) to quantify practice variations in ICP interventions. Primary endpoints were 6 month mortality and 6 month Glasgow Outcome Scale Extended (GOSE) score. A propensity score method with inverse probability of treatment weighting was used to estimate the association between use of ICP monitoring and these 6 month outcomes, independently of measured baseline covariates. This study is registered with ClinicalTrial.gov, NCT03257904. Findings: Between March 15, 2018, and April 30, 2019, 4776 patients were assessed for eligibility and 2395 patients were included in the study, including 1287 (54%) with traumatic brain injury, 587 (25%) with intracranial haemorrhage, and 521 (22%) with subarachnoid haemorrhage. The median age of patients was 55 years (IQR 39–69) and 1567 (65%) patients were male. Considerable variability was recorded in the use of ICP monitoring across centres (MOR 4·5, 95% CI 3·8–4·9 between two randomly selected centres for patients with similar covariates). 6 month mortality was lower in patients who had ICP monitoring (441/1318 [34%]) than in those who were not monitored (517/1049 [49%]; p<0·0001). ICP monitoring was associated with significantly lower 6 month mortality in patients with at least one unreactive pupil (hazard ratio [HR] 0·35, 95% CI 0·26–0·47; p<0·0001), and better neurological outcome at 6 months (odds ratio 0·38, 95% CI 0·26–0·56; p=0·0025). Median TIL was higher in patients with ICP monitoring (9 [IQR 7–12]) than in those who were not monitored (5 [3–8]; p<0·0001) and an increment of one point in TIL was associated with a reduction in mortality (HR 0·94, 95% CI 0·91–0·98; p=0·0011). Interpretation: The use of ICP monitoring and ICP management varies greatly across centres and countries. The use of ICP monitoring might be associated with a more intensive therapeutic approach and with lower 6-month mortality in more severe cases. Intracranial hypertension treatment guided by monitoring might be considered in severe cases due to the potential associated improvement in long-term clinical results. Funding: University of Milano-Bicocca and the European Society of Intensive Care Medicine