心电信号预处理与心电信号分析

本文介绍了在一种便携式心电监护仪器中是如何对心电数据进行预处理和智能分析的．为了适应便携式仪器的特征，我们在心电信号预处理中采用了FFT滤波和滑动平均滤波的方法去除各种干扰并使图像得以平滑，同时采用了差分阈值法提取特征点，考虑到监护仪器的实用性，在心电信号分析阶段，我们采用了分析特征间期异常情况的方法来替代对病症的智能诊断功能。     

致癌物分子结构识别中的一种特征提取方法

本文论述基于拓扑参数的致癌物分子结构识别中的一种特征参数－－特征片段的提取方法；对一组活性已知的分子训练集，将其分子结构用ＫＬＮ编码法进行编码并输入计算机，在三个约束条件下用深度第一法对各分子进行结构分解，生成片段文件，并汇总成片段总库，然后用统计检验和聚类分析提取和优化特征片段，从而为识别模型提供合理有效的特征参数。     

Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning

Objective

Intracranial electroencephalographic (iEEG) recordings contain “bad channels”, which show non-neuronal signals. Here, we developed a new method that automatically detects iEEG bad channels using machine learning of seven signal features.

A case series comparison of semantically focused vs. phonologically focused cued naming treatment in aphasia

There is a lack of clarity in the field regarding how to best predict which naming treatment will be most beneficial for a particular individual with aphasia. The purpose of this study was to elucidate whether or not semantic or phonological therapy differentially impacts on outcomes for people with a range of different aphasic profiles when given both therapies. A single-participant design, with multiple repeated baselines for naming, replicated across four participants, was used. Participants were provided with a counterbalanced order of Semantic Feature Analysis (SFA) and Phonological Components Analysis (PCA) treatment. Findings demonstrated differential effects across participants. This seemed to be influenced by factors such as severity of anomia, order of treatment presentation, and capacity limits. Clinical implications of these findings highlight the importance of expanding our picture of a participant’s behaviours to consider what other important factors can inform intervention decisions.     

基于数据挖掘的脑白质疏松症相关因素分析

目的 利用数据挖掘技术分析脑白质疏松症相关因素。      

Radiomic Feature Robustness and Reproducibility in Quantitative Bone Radiography: A Study on Radiologic Parameter Changes

The purpose of this study was to investigate the robustness of different radiography radiomic features over different radiologic parameters including kV, mAs, filtration, tube angles, and source skin distance (SSD). A tibia bone phantom was prepared and all imaging studies was conducted on this phantom. Different radiologic parameters including kV, mAs, filtration, tube angles, and SSD were studied. A region of interest was drawn on the images and many features from different feature sets including histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and wavelet derived parameters were extracted. All radiomic features were categorized based on coefficient of variation (COV). Bland-Altman analysis also was used to evaluate the mean, standard deviation, and upper/lower reproducibility limits for radiomic features in response to variation in each testing parameters. Results on COV in all features showed that 22%, 34%, and 45% of features were most robust (COV ≤ 5%) against kV, mAs, and SSD respectively and there was no robust features against filtration and tube angle. Also, all features (100%) and 76% of which showed large variations (COV > 20%) against filtrations and tube angle respectively. Autoregressive model feature set has no robust features against all radiologic parameters. Features including sum-average, sum-entropy, correlation, mean, and percentile (50, 90, and 99) belong to co-occurrence matrix and histogram feature sets were found as most robust features. Bland-Altman analysis showed the high reproducibity of some feature sets against radiologic parameter changes. The results presented here indicated that radiologic parameters have great impacts on radiomic feature values and caution should be taken into account when work with these features. In quantitative bone studies, robust features with low COV can be selected for clinical or research applications. Reproducible features also can be obtained using Bland-Altman analysis.     

Machine-based classification of ADHD and nonADHD participants using time/frequency features of event-related neuroelectric activity

Objective

Attention-deficit/hyperactivity disorder (ADHD) is the most frequent diagnosis among children who are referred to psychiatry departments. Although ADHD was discovered at the beginning of the 20th century, its diagnosis is still confronted with many problems.

临沂市2004～2009年流行性腮腺炎发病特征分析

目的分析临沂市2004～2009年以来流行性腮腺炎发病特征及趋势,为科学制定适合本地特点的防控措施提供依据。方法采用描述流行病学方法对全市历年流行性腮腺炎疫情监测资料进行分析,探索其流行特征和趋势。结果 2004～2009年全市共报告腮腺炎4460例,6年的发病率依次为5.37/10万、2.53/10万、4.18/10万、7.64/10万、28.03/10万和7.61/10万;平均发病率为9.29/10万,低于全国平均发病水平(19.88/10万),但高于本地2001～2003年平均发病水平(6.65/10万)。其流行特征为3～5月为发病高峰,占总数的36.6%,5～15岁为高发年龄段,占72.58%,发病职业以学生(占72.85%)为主,其次是幼托儿童(11.70%)和散居儿童(9.64%)。暴发疫情主要发生在小学,占暴发总次数的81.48%。结论流行性腮腺炎的易感人群仍为学龄儿童,应提高适龄儿童的预防接种率并加强对5～15岁儿童的腮腺炎疫苗的查漏补种工作,防止疫情暴发。     

Automatic evaluation of pressure sore status by combining information obtained from high-frequency ultrasound and digital photography

In this study, the different phases of pressure sore generation and healing are investigated through a combined analysis of high-frequency ultrasound (20 MHz) images and digital color photographs. Pressure sores were artificially induced in guinea pigs, and the injured regions were monitored for 21 days (data were obtained on days 3, 7, 14, and 21). Several statistical features of the images were extracted, relating to both the altering pattern of tissue and its superficial appearance. The features were grouped into five independent categories, and each category was used to train a neural network whose outputs were the four days. The outputs of the five classifiers were then fused using a fuzzy integral to provide the final decision. We demonstrate that the suggested method provides a better decision regarding tissue status than using either imaging technique separately. This new approach may be a viable tool for detecting the phases of pressure sore generation and healing in clinical settings.