基于多维特征和支持向量机核函数优化的自动化肺结节检测模型

为了解决特征级肺结节检测研究中的特征结构不合理和分类器性能低下两个问题,提出了一种多维特征表达与支持向量机(support vector machine,SVM)核函数优化相结合的自动化肺结节检测模型。首先提取多维特征数据量化感兴趣区域(region of interest,ROI),然后利用网格寻优算法优化SVM核函数,最后基于优化的SVM分类器识别结节区域和非结节区域。仿真实验结果表明,该模型耗时短、检测正确率高,具有一定的临床应用价值。     

Deep learning facilitates the diagnosis of adult asthma

BackgroundWe explored whether the use of deep learning to model combinations of symptom-physical signs and objective tests, such as lung function tests and the bronchial challenge test, would improve model performance in predicting the initial diagnosis of adult asthma when compared to the conventional machine learning diagnostic method.MethodsThe data were obtained from the clinical records on prospective study of 566 adult out-patients who visited Kindai University Hospital for the first time with complaints of non-specific respiratory symptoms. Asthma was comprehensively diagnosed by specialists based on symptom-physical signs and objective tests. Model performance metrics were compared to logistic analysis, support vector machine (SVM) learning, and the deep neural network (DNN) model.ResultsFor the diagnosis of adult asthma based on symptom-physical signs alone, the accuracy of the DNN model was 0.68, whereas that for the SVM was 0.60 and for the logistic analysis was 0.65. When adult asthma was diagnosed based on symptom-physical signs, biochemical findings, lung function tests, and the bronchial challenge test, the accuracy of the DNN model increased to 0.98 and was significantly higher than the 0.82 accuracy of the SVM and the 0.94 accuracy of the logistic analysis.ConclusionsDNN is able to better facilitate diagnosing adult asthma, compared with classical machine learnings, such as logistic analysis and SVM. The deep learning models based on symptom-physical signs and objective tests appear to improve the performance for diagnosing adult asthma.     

基于多尺度最大李雅普诺夫指数的表面肌电信号模式识别

为了提高动作表面肌电信号的识别率,提出一种将最大李雅普诺夫指数和多尺度分析结合的方法。从非线性和非平稳的角度出发,引入多尺度最大李雅普诺夫指数特征,并应用到人体前臂6类动作表面肌电信号的模式识别中。首先利用希尔伯特-黄变换,对原始信号进行经验模态分解,即多尺度分解;然后利用非线性时间序列分析方法,计算多尺度最大李雅普诺夫指数;最后将多尺度最大李雅普诺夫指数作为特征向量,输入支持向量机进行识别。平均识别率达到97.5%,比利用原始信号的最大李雅普诺夫指数进行识别时提高了3.9%。结果表明,利用多尺度最大李雅普诺夫指数对动作表面肌电信号进行模式识别效果良好。     

Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme

The objective of this study is to investigate the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. The availability of an automated computer analysis tool that is more objective than human readers can potentially lead to more reliable and reproducible brain tumor diagnostic procedures. A computer‐assisted classification method combining conventional MRI and perfusion MRI is developed and used for differential diagnosis. The proposed scheme consists of several steps including region‐of‐interest definition, feature extraction, feature selection, and classification. The extracted features include tumor shape and intensity characteristics, as well as rotation invariant texture features. Feature subset selection is performed using support vector machines with recursive feature elimination. The method was applied on a population of 102 brain tumors histologically diagnosed as metastasis ( 24 ), meningiomas ( 4 ), gliomas World Health Organization grade II ( 22 ), gliomas World Health Organization grade III ( 18 ), and glioblastomas ( 34 ). The binary support vector machine classification accuracy, sensitivity, and specificity, assessed by leave‐one‐out cross‐validation, were, respectively, 85%, 87%, and 79% for discrimination of metastases from gliomas and 88%, 85%, and 96% for discrimination of high‐grade (grades III and IV) from low‐grade (grade II) neoplasms. Multiclass classification was also performed via a one‐vs‐all voting scheme. Magn Reson Med, 2009. © 2009 Wiley‐Liss, Inc.     

基于FTIR离散平稳小波特征提取的SVM应用于中药材紫花地丁与同属植物的鉴别研究

目的 探索基于FTIR离散平稳小波变换结合支持向量机（support vector machine,SVM）分类法的中药紫花地丁的质量控制新模式。方法 采用衰减全反射傅里叶变换红外光谱法直接快速测定中药紫花地丁与同属植物多花堇菜和戟叶堇菜的FTIR,运用基于离散平稳小波变换进行特征向量的提取,通过分析比较后选取第4、5层分解层的特征向量用于支持向量机的训练与验证。结果 通过对不同产地的90个样本的验证,紫花地丁与同属植物多花堇菜和戟叶堇菜的识别率达100%。结论 基于FTIR离散平稳小波变换结合支持向量机分类法的中药紫花地丁与同属植物多花堇菜和戟叶堇菜的分类鉴别方法具有非常好的效果。     

A support vector machine for predicting defibrillation outcomes from waveform metrics

Background

Algorithms to predict shock success based on VF waveform metrics could significantly enhance resuscitation by optimising the timing of defibrillation.

Objective

To investigate robust methods of predicting defibrillation success in VF cardiac arrest patients, by using a support vector machine (SVM) optimisation approach.

Methods

Frequency-domain (AMSA, dominant frequency and median frequency) and time-domain (slope and RMS amplitude) VF waveform metrics were calculated in a 4.1Y window prior to defibrillation. Conventional prediction test validity of each waveform parameter was conducted and used AUC > 0.6 as the criterion for inclusion as a corroborative attribute processed by the SVM classification model. The latter used a Gaussian radial-basis-function (RBF) kernel and the error penalty factor C was fixed to 1. A two-fold cross-validation resampling technique was employed.

Results

A total of 41 patients had 115 defibrillation instances. AMSA, slope and RMS waveform metrics performed test validation with AUC > 0.6 for predicting termination of VF and return-to-organised rhythm. Predictive accuracy of the optimised SVM design for termination of VF was 81.9% (±1.24 SD); positive and negative predictivity were respectively 84.3% (±1.98 SD) and 77.4% (±1.24 SD); sensitivity and specificity were 87.6% (±2.69 SD) and 71.6% (±9.38 SD) respectively.

Conclusions

AMSA, slope and RMS were the best VF waveform frequency–time parameters predictors of termination of VF according to test validity assessment. This a priori can be used for a simplified SVM optimised design that combines the predictive attributes of these VF waveform metrics for improved prediction accuracy and generalisation performance without requiring the definition of any threshold value on waveform metrics.     

乳腺肿瘤超声图像识别模式分类方法的比较研究

目的利用乳腺肿瘤超声图像良恶性的不同特征,借助于模式分类方法对乳腺肿瘤良恶性进行识别,作为医生的计算机辅助诊断。方法本文研究基于乳腺肿瘤超声图像的原始特征参数已提取情况下,采用顺序前进搜索方法获得最优特征矢量,然后利用支撑矢量机、贝叶斯分类器、BP网络和Fisher线性判别器四种模式识别方法分别对乳腺肿瘤良恶性进行识别。结果基于200例病例随机划分为训练集100例和测试集100例进行测试,支撑矢量机、贝叶斯分类器、BP网络和Fisher线性判别器的Accuracy分别为0.960,0.940,0.932±0.013,0.930。结论支撑矢量机的分类性能优于其它分类器,能有效地对超声图像乳腺肿瘤进行良恶性识别。     

A comparison approach toward finding the best feature and classifier in cue-based BCI

In this paper, a comparative evaluation of state-of-the art feature extraction and classification methods is presented for five subjects in order to increase the performance of a cue-based Brain-Computer interface (BCI) system for imagery tasks (left and right hand movements). To select an informative feature with a reliable classifier features containing standard bandpower, AAR coefficients, and fractal dimension along with support vector machine (SVM), Adaboost and Fisher linear discriminant analysis (FLDA) classifiers have been assessed. In the single feature-classifier combinations, bandpower with FLDA gave the best results for three subjects, and fractal dimension and FLDA and SVM classifiers lead to the best results for two other subjects. A genetic algorithm has been used to find the best combination of the features with the aforementioned classifiers and led to dramatic reduction of the classification error and also best results in the four subjects. Genetic feature combination results have been compared with the simple feature combination to show the performance of the Genetic algorithm.