Acquired Immunodeficiency Syndrome ; Humans ; Markov Chains

Heart sound segmentation is a key step before heart sound classification. It refers to the processing of the acquired heart sound signal that separates the cardiac cycle into systolic and diastolic, etc. To solve the accuracy limitation of heart sound segmentation without relying on electrocardiogram, an algorithm based on the duration hidden Markov model (DHMM) was proposed. Firstly, the heart sound samples were positionally labeled. Then autocorrelation estimation method was used to estimate cardiac cycle duration, and Gaussian mixture distribution was used to model the duration of sample-state. Next, the hidden Markov model (HMM) was optimized in the training set and the DHMM was established. Finally, the Viterbi algorithm was used to track back the state of heart sounds to obtain S
Algorithms ; Electrocardiography ; Heart Sounds ; Markov Chains ; Normal Distribution

The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were “gemtc” for the Bayesian approach and “netmeta” for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the “rjags” package is a common tool. “rjags” implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for heterogeneity, moderator effects, and publication bias were reported using R software. The authors focus on two flexible models, Bayesian and frequentist, to determine overall effect sizes in network meta-analysis. This study focused on the practical methods of network meta-analysis rather than theoretical concepts, making the material easy to understand for Korean researchers who did not major in statistics. The authors hope that this study will help many Korean researchers to perform network meta-analyses and conduct related research more easily with R software.
Bayes Theorem ; Hope ; Markov Chains ; Population Characteristics ; Publication Bias

The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were “gemtc” for the Bayesian approach and “netmeta” for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the “rjags” package is a common tool. “rjags” implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for heterogeneity, moderator effects, and publication bias were reported using R software. The authors focus on two flexible models, Bayesian and frequentist, to determine overall effect sizes in network meta-analysis. This study focused on the practical methods of network meta-analysis rather than theoretical concepts, making the material easy to understand for Korean researchers who did not major in statistics. The authors hope that this study will help many Korean researchers to perform network meta-analyses and conduct related research more easily with R software.
Bayes Theorem ; Hope ; Markov Chains ; Population Characteristics ; Publication Bias

PURPOSE: Survival analysis of gastric cancer patients requires knowledge about factors that affect survival time. This paper attempted to analyze the survival of patients with incomplete registered data by using imputation methods. MATERIALS AND METHODS: Three missing data imputation methods, including regression, expectation maximization algorithm, and multiple imputation (MI) using Monte Carlo Markov Chain methods, were applied to the data of cancer patients referred to the cancer institute at Imam Khomeini Hospital in Tehran in 2003 to 2008. The data included demographic variables, survival times, and censored variable of 471 patients with gastric cancer. After using imputation methods to account for missing covariate data, the data were analyzed using a Cox regression model and the results were compared. RESULTS: The mean patient survival time after diagnosis was 49.1+/-4.4 months. In the complete case analysis, which used information from 100 of the 471 patients, very wide and uninformative confidence intervals were obtained for the chemotherapy and surgery hazard ratios (HRs). However, after imputation, the maximum confidence interval widths for the chemotherapy and surgery HRs were 8.470 and 0.806, respectively. The minimum width corresponded with MI. Furthermore, the minimum Bayesian and Akaike information criteria values correlated with MI (-821.236 and -827.866, respectively). CONCLUSIONS: Missing value imputation increased the estimate precision and accuracy. In addition, MI yielded better results when compared with the expectation maximization algorithm and regression simple imputation methods.
Diagnosis ; Drug Therapy ; Humans ; Markov Chains ; Proportional Hazards Models ; Stomach Neoplasms* ; Survival Analysis*

Due to the increasing interest in SNPs and mutational hot spots for disease traits, it is becoming more important to define and understand the relationship between SNPs and their flanking sequences. To study the effects of flanking sequences on SNPs, statistical approaches are necessary to assess bias in SNP data. In this study we mainly applied Markov chains for SNP sequences, particularly those located in intronic regions, and for analysis of in-del data. All of the pertaining sequences showed a significant tendency to generate particular SNP types. Most sequences flanking SNPs had lower complexities than average sequences, and some of them were associated with microsatellites. Moreover, many Alu repeats were found in the flanking sequences. We observed an elevated frequency of single-base-pair repeat-like sequences, mirror repeats, and palindromes in the SNP flanking sequence data. Alu repeats are hypothesized to be associated with C-to-T transition mutations or A-to-I RNA editing. In particular, the in-del data revealed an association between particular changes such as palindromes or mirror repeats. Results indicate that the mechanism of induction of in-del transitions is probably very different from that which is responsible for other SNPs. From a statistical perspective, frequent DNA lesions in some regions probably have effects on the occurrence of SNPs.
Bias (Epidemiology) ; DNA ; Humans* ; Introns ; Markov Chains ; Microsatellite Repeats ; Polymorphism, Single Nucleotide* ; RNA Editing

In order to deduce the sleep stages from heart rate, we analyze the heart rate variability (HRV) with hidden Markov model (HMM) for the identification of different characters of HRV within different sleep stages. Special technique is used to compensate the individual diversity. The relationship between the sleep stage and the ultra-low frequency components of HRV is also considered. Since the detection of heart rate hardly disturbs the sleep, the proposed method provides a simple approach to evaluating the sleep stage without disturbing the sleep. Our experiments have proved that this method meets the requirements of wide applications, especially the requirement of routine use in monitoring the normal subjects' sleep.
Adult ; Electrocardiography ; Heart Rate ; Humans ; Markov Chains ; Middle Aged ; Models, Biological ; Sleep Stages ; physiology

OBJECTIVEExtraction of cepstral coefficients combined with Gaussian Mixture Model (GMM) is used to propose a biometric method based on heart sound signal.

METHODSFirstly, the original heart sounds signal was preprocessed by wavelet denoising. Then, Linear Prediction Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) are compared to extract representative features and develops hidden Markov model (HMM) for signal classification. At last, the experiment collects 100 heart sounds from 50 people to test the proposed algorithm.

RESULTSThe comparative experiments prove that LPCC is more suitable than MFCC for heart sound biometric, and by wavelet denoising in each piece of heart sound signal, the system achieves higher recognition rate than traditional GMM.

CONCLUSIONThose results show that this method can effectively improve the recognition performance of the system and achieve a satisfactory effect.

Algorithms ; Biometry ; Heart ; physiology ; Humans ; Markov Chains ; Models, Biological ; Phonocardiography ; methods ; Wavelet Analysis

PURPOSE: To perform an economic evaluation of the different treatment methods available for primary open-angle glaucoma in a Korean setting, including medication, selective laser trabeculoplasty, or surgery. METHODS: Three independent Markov chains were constructed for each treatment option to simulate treatment progress and to evaluate the total treatment costs for each initial strategy. The Markov chain consisted of different stages (5, 10, 20 stages), with each stage being one year. Assuming 1000 patients, a Monte Carlo simulation was iterated 1000 times to evaluate the cost of treatment over 5, 10 and 20 years. RESULTS: During the initial five years, medication as the initial treatment was the most expensive, whereas laser trabeculoplasty was the cheapest. After ten years, surgery became the cheapest treatment. In ten years, if the success rate of surgery is greater than 30.1%, it was more economic to choose surgery as the initial treatment. For laser trabeculoplasty, if the success rate was greater than 16.3%, laser treatment was more economical than was medication. Our model shows that only if the annual cost of medication decreases to 60,000 won or 55,000 won, then the cost of choosing medication as the initial treatment strategy will be more economical than that of laser therapy or surgery, respectively. CONCLUSIONS: The economic value of choosing laser therapy as the initial treatment strategy is the greatest over five simulated-years, whereas surgery had the greatest economic value over more than ten years.
Glaucoma ; Glaucoma, Open-Angle ; Health Care Costs ; Humans ; Laser Therapy ; Markov Chains ; Trabeculectomy

PURPOSE: To estimate the cost-effectiveness of treatment with early surgical therapy for primary open-angle glaucoma compared with early medical therapy. METHODS: The hypothetical patient was treated with early surgical or early medical therapy. Relevant costs were obtained from 2002 National Health Insurance data. The success rate of each therapy was obtained from previous reports. Cost-effective Markov model was created using the medical decision analysis program. Markov approach simulated disease progression. Matrix calculation and Monte Carlo simulation were used to determine whether there was a significant difference in quality-of-life adjusted years (QALYs) gained between surgical and medical therapy. RESULTS: In Moorfields study cost-effectiveness for early surgical therapy and medical monotherapy were 110, 161 W/QALYs and 189, 616 W/QALYs, respectively. In the Collaborative Initial Glaucoma Treatment Study (CIGTS) cost-effectiveness for early surgical therapy and medical monotherapy were 153, 578 W/QALYs and 201, 353 W/QALYs, respectively. Matrix calculation and Monte Carlo simulation showed that early surgical therapy was superior cost-effective treatment to medical therapy. CONCLUSIONS: By cost-effective Markov medical decision model, early surgical therapy for primary open-angle glaucoma is a cost-effective treatment option and is superior to medical therapy.
Decision Support Techniques ; Disease Progression ; Glaucoma ; Glaucoma, Open-Angle* ; Humans ; Markov Chains ; National Health Programs