An Application of a Hybrid Intelligent System for Diagnosing Primary Headaches.

(1) Background: Modern medicine generates a great deal of information that stored in medical databases. Simultaneously, extracting useful knowledge and making scientific decisions for diagnosis and treatment of diseases becomes increasingly necessary. Headache disorders are the most prevalent of all the neurological conditions. Headaches have not only medical but also great socioeconomic significance. The aim of this research is to develop an intelligent system for diagnosing primary headache disorders. (2) Methods: This research applied various mathematical, statistical and artificial intelligence techniques, among which the most important are: Calinski-Harabasz index, Analytical Hierarchy Process, and Weighted Fuzzy C-means Clustering Algorithm. These methods, techniques and methodologies are used to create a hybrid intelligent system for diagnosing primary headache disorders. The proposed intelligent diagnostic system is tested with original real-world data set with different metrics. (3) Results: First at all, nine of 20 attributes - features from International Headache Society (IHS) criteria are selected, and then only five most important attributes from IHS criteria are selected. The calculation result based on the Calinski-Harabasz index value (178) for the optimal number of clusters is three, and they present three classes of headaches: (i) migraine, (ii) tension-type headaches (TTHs), and (iii) other primary headaches (OPHs). The proposed hybrid intelligent system shows the following quality metrics: Accuracy 75%; Precision 67% for migraine, 74% for TTHs, 86% for OPHs, and Average Precision 77%; Recall 86% for migraine, 73% for TTHs, 67% for OPHs, Average Recall 75%; F1 score 75% for migraine, 74% for TTHs, 75% for OPHs, and Average F1 score 75%. (4) Conclusions: The hybrid intelligent system presents qualitative and respectable experimental results. The implementation of existing diagnostics systems and the development of new diagnostics systems in medicine is necessary in order to help physicians make quality diagnosis and decide the best treatments for the patients.

Impact of Individual Headache Types on the Work and Work Efficiency of Headache Sufferers.

Background: Headaches have not only medical but also great socioeconomic significance, therefore, it is necessary to evaluate the overall impact of headaches on a patient's life, including their work and work efficiency. The aim of this study was to determine the impact of individual headache types on work and work efficiency. Methods: This research was designed as a cross-sectional study performed by administering a questionnaire among employees. The questionnaire consisted of general questions, questions about headache features, and questions about the impact of headaches on work. Results: Monthly absence from work was mostly represented by migraine sufferers (7.1%), significantly more than with sufferers with tension-type headaches (2.23%; p = 0.019) and other headache types (2.15%; p = 0.025). Migraine sufferers (30.2%) worked in spite of a headache for more than 25 h, which was more frequent than with sufferers from tension-type and other-type headaches (13.4%). On average, headache sufferers reported work efficiency ranging from 66% to 90%. With regard to individual headache types, this range was significantly more frequent in subjects with tension-type headaches, whereas 91-100% efficiency was significantly more frequent in subjects with other headache types. Lower efficiency, i.e., 0-40% and 41-65%, was significantly more frequent with migraine sufferers. Conclusions: Headaches, especially migraines, significantly affect the work and work efficiency of headache sufferers by reducing their productivity. Loss is greater due to reduced efficiency than due to absenteeism.

Hemicranial Cough-Induced Headache as a First Symptom of a Carotid-Cavernous Fistula-Case Report.

Background and objectives: Spontaneous carotid-cavernous fistulas (CCFs) are rare, and they may be caused by an aneurysm rupture. Materials and Methods: A case of a man hospitalized for high-intensity hemicranial headache with sudden cough onset as part of an upper respiratory tract infection is presented. The pain was of a pulsating character, localized on the right, behind the eye, followed by nausea and vomiting. Neurological finding registered a wider rima oculi to the right and slight neck rigidity. Laboratory findings detected a mild leukocytosis with neutrophil predominance, while cytobiochemical findings of CSF and a computerized tomography (CT) scan of the endocranium were normal. Results: Magnetic resonance imaging (MRI) angiography indicated the presence of a carotid cavernous fistula with a pseudoaneurysm to the right. Digital subtraction angiography (DSA) was performed to confirm the existence of the fistula. The planned artificial embolization was not performed because a complete occlusion of the fistula occurred during angiographic examination. Patient was discharged without subjective complaints and with normal neurological findings. Conclusions: Hemicranial cough-induced headache may be the first sign of carotid cavernous fistula, which was resolved by a spontaneous thrombosis in preparation for artificial embolization.

Improving Fall Detection Using an On-Wrist Wearable Accelerometer.

Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are detected using a published threshold-based solution, although a study on threshold tuning has been carried out. The feature extraction is extended in order to balance the dataset for the minority class. Alternative models have been analyzed to reduce the computational constraints so the solution can be embedded in smart-phones or smart wristbands. Several published datasets have been used in the Materials and Methods section. Although these datasets do not include data from real falls of elderly people, a complete comparison study of fall-related datasets shows statistical differences between the simulated falls and real falls from participants suffering from impairment diseases. Given the obtained results, the rule-based systems represent a promising research line as they perform similarly to neural networks, but with a reduced computational cost. Furthermore, support vector machines performed with a high specificity. However, further research to validate the proposal in real on-line scenarios is needed. Furthermore, a slight improvement should be made to reduce the number of false alarms.

Generalized Models for the Classification of Abnormal Movements in Daily Life and its Applicability to Epilepsy Convulsion Recognition.

The identification and the modeling of epilepsy convulsions during everyday life using wearable devices would enhance patient anamnesis and monitoring. The psychology of the epilepsy patient penalizes the use of user-driven modeling, which means that the probability of identifying convulsions is driven through generalized models. Focusing on clonic convulsions, this pre-clinical study proposes a method for generating a type of model that can evaluate the generalization capabilities. A realistic experimentation with healthy participants is performed, each with a single 3D accelerometer placed on the most affected wrist. Unlike similar studies reported in the literature, this proposal makes use of [Formula: see text] cross-validation scheme, in order to evaluate the generalization capabilities of the models. Event-based error measurements are proposed instead of classification-error measurements, to evaluate the generalization capabilities of the model, and Fuzzy Systems are proposed as the generalization modeling technique. Using this method, the experimentation compares the most common solutions in the literature, such as Support Vector Machines, [Formula: see text]-Nearest Neighbors, Decision Trees and Fuzzy Systems. The event-based error measurement system records the results, penalizing those models that raise false alarms. The results showed the good generalization capabilities of Fuzzy Systems.

Improving human activity recognition and its application in early stroke diagnosis.

The development of efficient stroke-detection methods is of significant importance in today's society due to the effects and impact of stroke on health and economy worldwide. This study focuses on Human Activity Recognition (HAR), which is a key component in developing an early stroke-diagnosis tool. An overview of the proposed global approach able to discriminate normal resting from stroke-related paralysis is detailed. The main contributions include an extension of the Genetic Fuzzy Finite State Machine (GFFSM) method and a new hybrid feature selection (FS) algorithm involving Principal Component Analysis (PCA) and a voting scheme putting the cross-validation results together. Experimental results show that the proposed approach is a well-performing HAR tool that can be successfully embedded in devices.

A cluster merging method for time series microarray with production values.

A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

Reduced sTWEAK and increased sCD163 levels in HIV-infected patients: modulation by antiretroviral treatment, HIV replication and HCV co-infection.

BACKGROUND: Patients infected with the human immunodeficiency virus (HIV) have an increased risk of cardiovascular disease due to increased inflammation and persistent immune activation. CD163 is a macrophage scavenger receptor that is involved in monocyte-macrophage activation in HIV-infected patients. CD163 interacts with TWEAK, a member of the TNF superfamily. Circulating levels of sTWEAK and sCD163 have been previously associated with cardiovascular disease, but no previous studies have fully analyzed their association with HIV. OBJECTIVE: The aim of this study was to analyze circulating levels of sTWEAK and sCD163 as well as other known markers of inflammation (hsCRP, IL-6 and sTNFRII) and endothelial dysfunction (sVCAM-1 and ADMA) in 26 patients with HIV before and after 48 weeks of antiretroviral treatment (ART) and 23 healthy subjects. RESULTS: Patients with HIV had reduced sTWEAK levels and increased sCD163, sVCAM-1, ADMA, hsCRP, IL-6 and sTNFRII plasma concentrations, as well as increased sCD163/sTWEAK ratio, compared with healthy subjects. Antiretroviral treatment significantly reduced the concentrations of sCD163, sVCAM-1, hsCRP and sTNFRII, although they remained elevated when compared with healthy subjects. Antiretroviral treatment had no effect on the concentrations of ADMA and sTWEAK, biomarkers associated with endothelial function. The use of protease inhibitors as part of antiretroviral therapy and the presence of HCV-HIV co-infection and/or active HIV replication attenuated the ART-mediated decrease in sCD163 plasma concentrations. CONCLUSION: HIV-infected patients showed a proatherogenic profile characterized by increased inflammatory, immune-activation and endothelial-dysfunction biomarkers that partially improved after ART. HCV-HIV co-infection and/or active HIV replication enhanced immune activation despite ART.