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Abstract : Model Based Sparse Feature Extraction for Biomedical Signal Classification1
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Abstract: This article focuses on model based sparse feature extraction of biomedical signals for classification problems, which stems from sparse representation in modern signal processing. In the presented work, a novel approach based on sparse principal component analysis (SPCA) is proposed to extract signal features. This method involves partitioning signals and utilizing SPCA to select only a limited number of signal segments in order to construct signal principal components during the training stage. For signal classification purposes, a set of regression models based on sparse principal components of the selected training signal segments is constructed. Within this approach, model residuals are estimated and used as signal features for classification. The applications of the proposed approach are demonstrated by using both the synthetic data and real EEG signals. The high classification accuracy results suggest that the proposed methods may be useful for automatic event detection using long-term observational signals. keywords: Sparse Principal Component Analysis, Sparse Feature Extraction, Signal Classification, Long-term Signals. Keywords: Sparse Principal Component Analysis, Sparse Representation, Signal Classification, Long-term Signals.Download Full Article |
Abstract : Robust Cox Regression as an Alternative Method to Estimate Adjusted Relative Risk in Prospective Studies with Common Outcomes
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Abstract: Objective: To demonstrate the use of robust Cox regression in estimating adjusted relative risks (and confidence intervals) when all participants with an identical follow-up time and when a common outcome is investigated. Methods: In this paper, we propose an alternative statistical method, robust Cox regression, to estimate adjusted relative risks in prospective studies. We use simulated cohort data to examine the suitability of robust Cox regression. Results: Robust Cox regression provides estimates that are equivalent to those of modified Poisson regression: regression coefficients, relative risks, 95% confidence intervals, P values. It also yields reasonable probabilities (bounded by 0 and 1). Unlike modified Poisson regression, robust Cox regression allows for four automatic variable selection methods, it directly computes adjusted relative risks for continuous variables, and is able to incorporate time-dependent covariates. Conclusion: Given the popularity of Cox regression in the medical and epidemiological literature, we believe that robust Cox regression may gain wider acceptance and application in the future. We recommend robust Cox regression as an alternative analytical tool to modified Poisson regression. In this study we demonstrated its utility to estimate adjusted relative risks for common outcomes in prospective studies with two or three waves of data collection (spaced similarly). Keywords: Robust Cox regression, Modified Poisson regression, Logistic regression, Relative risk, Odds ratio.Download Full Article |
Abstract : Parametric Modeling of Survival Data Based on Human Immune Virus (HIV) Infected Adult Patients under Highly Active Antiretroviral Therapy (HAART): A Case of Zewditu Referral Hospital, Addis Ababa (AA), Ethiopia
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Abstract: In the present article our aim is to model the HIV infected adult patients’ dataset. A retrospective cohort study was conducted in Zewditu Referral Hospital located in Addis Ababa, Ethiopia. Records of patients enrolled between September 2010 and August 2014 were reviewed continuously using patients’Antiretroviral Therapy (ART) unique identification numbers as reference. Kaplan-Meier survival curves and Log-Rank test were used to compare the survival experience of different category of patients. Then we attempted to model the above data with the help of four parametric models namely; Exponential, Weibull, Gompertz, and Log-logistic. All fitted models were compared separately by using AIC and log likelihood. The log-logistic model gave a better description of the time-to-death of HIV infected adult patients than the other models. Based on log-logistic model, age, weight, and functional status, TB screen, World Health Organization (WHO) clinical stage and educational level were found to be the most prognostic factors of time-to-death. Furthermore a high risk of death of patients was found to be associated with lower initial weight, WHO clinical stage IV, lower CD4 count, being ambulatory, bedridden, and TB screened and illiterate. Keywords: Human immunodeficiency Virus, Acquired immune deficiency syndrome, Parametric Models, HAART, ARTCD.Download Full Article |
Abstract : Perceptions about the Health Effects of Passive Smoking among Bangladeshi Young Adults
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Abstract: Passive smoking is now firmly established as a significant cause of morbidity and mortality. Assessment of young adults’ perceptions, understanding and knowledge of the health effects of passive smoking may promote educational endeavours to increase awareness of the passive smoking-linked health effects and to facilitate interventions. The study, therefore, assessed the perceptions of young adults in Bangladesh about the health effects of passive smoking. This cross-sectional descriptive study was conducted among 656 young adults in two districts under Dhaka division of Bangladesh. The study used a multistage cluster random sampling approach. Binary logistic regression was used for identifying the predictors of perceptions that passive smoking is harmful. The vast majority of the respondents believed that passive smoking causes illnesses but the knowledge of specific health effects was limited. Most (87.2%) respondents perceived that passive smoking causes ‘some’ or ‘a lot’ of harm to health of both adults and children. However, disparities in perceptions were prevalent across their educational levels. The results of logistic regression analysis showed that, after adjusting other factors, respondents who had nine or more years of education were 6.7 times likelihood of perceiving that passive smoking causes “some” or “lot of harm” compared to those who had no education. The findings suggested that more efforts, including some appropriate measures to address knowledge gaps, are needed to increase better perception about the harmful effects of passive smoking among young adults. Keywords: Robust Cox regression, Modified Poisson regression, Logistic regression, Relative risk, Odds ratio.Download Full Article |
