血氧测量快速滤波与实时算法设计

目的血氧饱和度是人体的重要参数,为适应无线监护系统的需要,针对无线血氧测量节点内存小、运算速度慢的特点,本文设计了一套递推型快速处理算法。方法该算法充分利用递推方式和形态滤波算法过程的中间结果,加速滤波算法的处理速度,提取出测量和控制的关键数据,保证了数据的快速处理和测量系统的实时控制。结果通过分析采样数据和模拟处理过程发现,该算法可有效消除基线漂移、抖动等干扰,保证测量的稳定可靠。结论该算法计算量小,抗干扰能力强,适合无线血氧测量节点实时测量的需求。     

Filtering big data from social media – Building an early warning system for adverse drug reactions

ObjectivesAdverse drug reactions (ADRs) are believed to be a leading cause of death in the world. Pharmacovigilance systems are aimed at early detection of ADRs. With the popularity of social media, Web forums and discussion boards become important sources of data for consumers to share their drug use experience, as a result may provide useful information on drugs and their adverse reactions. In this study, we propose an automated ADR related posts filtering mechanism using text classification methods. In real-life settings, ADR related messages are highly distributed in social media, while non-ADR related messages are unspecific and topically diverse. It is expensive to manually label a large amount of ADR related messages (positive examples) and non-ADR related messages (negative examples) to train classification systems. To mitigate this challenge, we examine the use of a partially supervised learning classification method to automate the process.MethodsWe propose a novel pharmacovigilance system leveraging a Latent Dirichlet Allocation modeling module and a partially supervised classification approach. We select drugs with more than 500 threads of discussion, and collect all the original posts and comments of these drugs using an automatic Web spidering program as the text corpus. Various classifiers were trained by varying the number of positive examples and the number of topics. The trained classifiers were applied to 3000 posts published over 60 days. Top-ranked posts from each classifier were pooled and the resulting set of 300 posts was reviewed by a domain expert to evaluate the classifiers.ResultsCompare to the alternative approaches using supervised learning methods and three general purpose partially supervised learning methods, our approach performs significantly better in terms of precision, recall, and the F measure (the harmonic mean of precision and recall), based on a computational experiment using online discussion threads from Medhelp.ConclusionsOur design provides satisfactory performance in identifying ADR related posts for post-marketing drug surveillance. The overall design of our system also points out a potentially fruitful direction for building other early warning systems that need to filter big data from social media networks.     

Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.     

基于“互联网+三师共管”的高血压慢病管理研究与应用

目的:借助“互联网+”优势提升传统医疗服务行业的品质和服务水平,为广大慢病患者提供安全、可靠、持续的健康管理服务。方法:提出了“互联网+三师共管”的高血压数字诊疗系统架构,并提出一种协同过滤推荐算法。结果:在远程诊疗服务推荐应用中,基于用户兴趣聚类协同过滤推荐算法的用户预测评分误差较小,推荐准确率最高。结论:所提出的基于“互联网+三师共管”的高血压慢病管理系统,能够更大程度地提高慢病管理服务水平和远程医疗效率。     

Calculus of variations approach for state and parameter estimation in switched 1D hyperbolic PDEs

This paper proposes the use of calculus of variations to solve the problem of state and parameter estimation for a class of switched 1‐dimensional hyperbolic partial differential equations coupled with an ordinary differential equation. The term “switched” here refers to a system changing its characteristics according to a switching rule, which may depend on time, parameters of the system, and/or state of the system. The estimation method is based on a smooth approximation of the system dynamics and the use of variational calculus on an augmented Lagrangian cost functional to get the sensitivity with respect to the initial state and some (possibly distributed) parameters of the system. Those sensitivities or variations, together with related adjoint systems, are used as inputs for an optimization algorithm to identify the values of the variables to be estimated. Two examples are provided to demonstrate the effectiveness of the proposed method. The first one is concerned with a switched overland flow model, developed from Saint‐Venant equations and Green‐Ampt law; the second example deals with a switched free traffic flow model based on the Lighthill‐Whitham‐Richards representation, modified by the presence of a relief route.     

L2‐L∞ filtering for a class of stochastic nonlinear systems with aperiodic sampling

This work investigates the problem of L₂‐L_∞ filtering for a class of stochastic nonlinear systems with nonuniform sampling. The sampled‐data filter developed in this paper is an impulsive differential system whose states change abruptly at every sampling instant. The resulting filtering error system is modeled as a stochastic nonlinear impulsive differential system. The goal is to propose a method for designing a target filter that ensures the stochastic asymptotic stability of the filtering error system and guarantees a prescribed L₂‐L_∞ performance. Based on a time‐varying Lyapunov functional, by virtue of a convex combination technique, a design method to achieve such a filter is formulated in the form of solving a set of linear matrix inequalities. The effectiveness of the proposed filtering strategy is shown via a numerical example of a stochastic Chua's circuit system.     

Free-breathing 3D whole-heart black-blood imaging with motion sensitized driven equilibrium

Purpose:

To automatically analyze the time course of collateralization in a rat hindlimb ischemia model based on signal intensity distribution (SID).

Materials and Methods:

Time‐of‐flight magnetic resonance angiograms (TOF‐MRA) were acquired in eight rats at 2, 7, and 21 days after unilateral femoral artery ligation. Analysis was performed on maximum intensity projections filtered with multiscale vessel enhancement filter. Differences in SID between ligated limb and a reference region were monitored over time and compared to manual collateral artery identification.

Results:

The differences in SID correlated well with the number of collateral arteries found with manual quantification. The time courses of ultrasmall (diameter ?0.5 mm) and small (diameter ≈0.5 mm) collateral artery development could be differentiated, revealing that maturation of the collaterals and enlargement of their feeding arteries occurred mainly after the first week postligation.

Conclusion:

SID analysis performed on axial maximum intensity projections is easy to implement, fast, and objective and provides more insight in the time course of arteriogenesis than manual identification. J. Magn. Reson. Imaging 2012;379‐386. © 2011 Wiley Periodicals, Inc.

     

The Clinical Incidence and Significance of Myopotential Sensing with Unipolar Pacemakers

Myopotential oversensing by unipolar pulse generators can cause patient symptoms ranging from dizziness and syncope to ventricular tachycardia. Seventy-seven patients with implanted unipolar pacemakers from three manufacturers participated in isometric and reach exercises to evaluate their pacemakers susceptibility to myopotentials. Myopotential inhibition occurred in 47% of the patients performing the reach maneuver. Testing revealed a wide difference in level of susceptibility to myopotentials between pacemakers of different manufacture (a low of 33% inhibition for the least susceptible to a high of 78% inhibition for the most susceptible during the reach maneuver). The normal pacing interval was extended by myopotential oversensing for each manufacturer's model within a range of 0.3–3.9 seconds. Pulse generators incorporating additional automatically-adjusting threshold and reversion circuits in the sense amplifier along with standard bandpass filtering exhibited: a) two-to-three times less susceptibility to myopotentials; and b) a 75% reduction in the maximum pacing interval extension as compared with pacemakers with bandpass filtering alone. The effectiveness of insulative coating in reducing myopotential inhibition was substantiated as coated pulse generators had a 22% lower incidence of muscle sensing than those than were uncoated. Six out of seven patients tested had symptoms during Holter monitoring which correlated with pacemaker myopotential inhibition. Selecting pulse generators with improved sensing amplifiers, clinical testing of patients with unipolar pacemakers using the reach method, and reprogramming of sensitivity will significantly reduce the incidence of myopotential inhibition.