Non-Laboratory-Based Risk Factors for Automated Heart Disease Detection

© 2018 IEEE. Developing a heart disease detection model using simple non-laboratory risk factors plays an important role in preventive care, especially for high risk subjects. The model allows physicians/epidemiologists to effectively diagnose a person as having heart disease. In this work, we aim to develop a non-invasive risk prediction model for automated heart disease detection that involves age, gender, rest blood pressure, maximum heart rate, and rest electrocardiography. We examine four public datasets from 1071 participants who were referred for a special X-ray of the heart's arteries (i.e., to see if they are narrowed or blocked). The subjects also undertook a physical examination and three non-invasive tests. To estimate the heart disease status, we apply a generalized linear model with regularization paths via coordinate descent. Even without laboratory-based data (e.g., serum cholesterol, fasting blood sugar), we observed a prediction accuracy as high as 72%, compared with 76% of other comprehensive models. This observation suggests that few non-invasive factors utilizing recent advances in data analytics can replace the current practices of heart disease risk assessment

Subject-Independent ERP-Based Brain-Computer Interfaces

© 2001-2011 IEEE. Brain-computer interfaces (BCIs) are desirable for people to express their thoughts, especially those with profound disabilities in communication. The classification of brain patterns for each different subject requires an extensively time-consuming learning stage specific to that person, in order to reach satisfactory accuracy performance. The training session could also be infeasible for disabled patients as they may not fully understand the training instructions. In this paper, we propose a unified classification scheme based on ensemble classifier, dynamic stopping, and adaptive learning. We apply this scheme on the P300-based BCI, with the subject-independent manner, where no learning session is required for new experimental users. According to our theoretical analysis and empirical results, the harmonized integration of these three methods can significantly boost up the average accuracy from 75.00% to 91.26%, while at the same time reduce the average spelling time from 12.62 to 6.78 iterations, approximately to two-fold faster. The experiments were conducted on a large public dataset which had been used in other related studies. Direct comparisons between our work with the others' are also reported in details

The REDD+ policy arena in Vietnam: participation of policy actors

Reducing emissions from deforestation and degradation (REDD+) has gained increasing global attention because of its potential to reduce carbon emissions and improve forest governance. Reducing emissions from deforestation and degradation requires successful inclusive decision making and accountability. However, there have been limited empirical studies that examine the effectiveness of the current participatory mechanism used in REDD+. Our research analyzes the participation of policy actors in the development of the REDD+ instrument in Vietnam. We are interested in how the political context and the different interests of actors influence the degree of participation in national REDD+ policy decision making. We explored participation through the analysis of the mechanisms, e.g., how actors involve and participate in decision making, and dynamics of participation, e.g., highly centralized policy event vs. donor led event. The study aims to answer three research questions: (1) Who is involved in national REDD+ policy making and what are their interests in participating in core political events? (2) What level of participation do the different political actors have in core political events? and (3) To what extent do the outcomes, e.g., regulations and strategies, of REDD+ policy events incorporate different preferences of policy actors? Our findings highlighted the dominant role of government agencies in REDD+ policy making, which leaves limited political space for nonstate actors, e.g., NGOs and civil society organizations (CSOs), in Vietnam to exert an influence on the final policy outputs. Even in this highly centralized context, however, we found evidence to suggest that some political space in decision making is given to nonstate actors. Within this space, such actors are able to propose alternative policy options. Ensuring inclusive decision making and accountability in the Vietnam context requires a shift in current governance from traditional top-down approaches to a more participatory form of decision making

Feature Analysis for Discrimination of Motor Unit Action Potentials

© 2018 IEEE. In electrophysiological signal processing for intramuscular electromyography data (nEMG), single motor unit activity is of great interest. The changes of action potential (MUAP) morphology, motor unit (MU) activation, and recruitment provide the most informative part to study the nature causality in neuromuscular disorders. In practice, for a single nEMG recording, more than one motor unit activities (in the surrounding area of a needle electrode) are usually collected. Such a fact makes the MUAP discrimination that separates single unit activities a crucial task. Most neurology laboratories worldwide still recruit specialists who spend hours to manually or semi-automatically sort MUAPs. From a machine learning perspective, this task is analogous to the clustering-based classification problem in which the number of classes and other class information are unfortunately missing. In this paper, we present a feature analysis strategy to help better utilize unsupervised (i.e., totally automated) methods for MUAP discrimination. To that end, we extract a large pool of features from each MUAP. Then we select the top ranked candidates using clusterability scores as selection criteria. We found spectrograms of wavelet decomposition as a top-ranking feature, highly correlated to the motor unit reference and was more separable than existing features. Using a correlation-based clustering technique, we demonstrate the sorting performance with this feature set. Compared with the reference produced by human experts, our method obtained a comparable result (e.g., equivalent number of classes was found, identical MUAP morphology in each pair of corresponding MU class, and similar histograms of MUs). Taking the manual labels as references, our method got a much higher sensitivity and accuracy than the compared unsupervised sorting method. We obtained a similar result in MUAP classification to the reference

A profiling analysis of contributions of cigarette smoking, dietary calcium intakes, and physical activity to fragility fracture in the elderly

© 2018 The Author(s). Fragility fracture and bone mineral density (BMD) are influenced by common and modifiable lifestyle factors. In this study, we sought to define the contribution of lifestyle factors to fracture risk by using a profiling approach. The study involved 1683 women and 1010 men (50+ years old, followed up for up to 20 years). The incidence of new fractures was ascertained by X-ray reports. A "lifestyle risk score" (LRS) was derived as the weighted sum of effects of dietary calcium intake, physical activity index, and cigarette smoking. Each individual had a unique LRS, with higher scores being associated with a healthier lifestyle. Baseline values of lifestyle factors were assessed. In either men or women, individuals with a fracture had a significantly lower age-adjusted LRS than those without a fracture. In men, each unit lower in LRS was associated with a 66% increase in the risk of total fracture (non-adjusted hazard ratio [HR] 1.66; 95% CI, 1.26 to 2.20) and still significant after adjusting for age, weight or BMD. However, in women, the association was uncertain (HR 1.30; 95% CI, 1.11 to 1.53). These data suggest that unhealthy lifestyle habits are associated with an increased risk of fracture in men, but not in women, and that the association is mediated by BMD

Freezing of Gait Detection in Parkinson's Disease: A Subject-Independent Detector Using Anomaly Scores

© 2012 IEEE. Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of 96% (79%). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of 94% (84%) for ankle and 89% (94%) for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., 3 s versus 7.5 s) and/or lower tolerance (e.g., 0.4 s versus 2 s)

Blockchain-based Secure Platform for Coalition Loyalty Program Management

In this paper, we propose a novel blockchain-based platform for the coalition loyalty program management. The platform allows the customers to freely exchange loyalty points from different existing blockchain-based loyalty programs by utilizing the sidechain technology. Moreover, by adopting the Proof-of-Stake consensus mechanism, we can further increase customer engagement by allowing the customers to participate in the consensus process to earn additional tokens. However, this might lead to situations where the customers centralize all tokens to a single chain/loyalty program if the chain offers more rewards for consensus participation. Through security and performance analyses, we show that such centralization of stakes poses a threat to the security and performance of the platform. Therefore, we develop a non-cooperative game model to analyze the rational behavior of the users. We reveal that the consensus participation rewards govern the user behavior and the decentralization of the system. Numerical experiments confirm our analytical results and show that the ratios between the consensus rewards have a significant impact on the system’s security and performance

Impact of a hospice rapid response service on preferred place of death, and costs

Background: Many people with a terminal illness would prefer to die at home. A new palliative rapid response service (RRS) provided by a large hospice provider in South East England was evaluated (2010) to provide evidence of impact on achieving preferred place of death and costs. The RRS was delivered by a team of trained health care assistants and available 24/7. The purpose of this study was to (i) compare the characteristics of RRS users and non-users, (ii) explore differences in the proportions of users and non-users dying in the place of their choice, (iii) monitor the whole system service utilisation of users and non-users, and compare costs. Methods: All hospice patients who died with a preferred place of death recorded during an 18 month period were included. Data (demographic, preferences for place of death) were obtained from hospice records. Dying in preferred place was modelled using stepwise logistic regression analysis. Service use data (period between referral to hospice and death) were obtained from general practitioners, community providers, hospitals, social services, hospice, and costs calculated using validated national tariffs. Results: Of 688 patients referred to the hospice when the RRS was operational, 247 (35.9 %) used it. Higher proportions of RRS users than non-users lived in their own homes with a co-resident carer (40.3 % vs. 23.7 %); more non-users lived alone or in residential care (58.8 % vs. 76.3 %). Chances of dying in the preferred place were enhanced 2.1 times by being a RRS user, compared to a non-user, and 1.5 times by having a co-resident carer, compared to living at home alone or in a care home. Total service costs did not differ between users and non-users, except when referred to hospice very close to death (users had higher costs). Conclusions: Use of the RRS was associated with increased likelihood of dying in the preferred place. The RRS is cost neutral