Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

The use of 3D surface fitting for robust polyp detection and classification in CT colonography

In this paper we describe the development of a computationally efficient computer-aided detection (CAD) algorithm based on the evaluation of the surface morphology that is employed for the detection of colonic polyps in computed tomography (CT) colonography. Initial polyp candidate voxels were detected using the surface normal intersection values. These candidate voxels were clustered using the normal direction, convexity test, region growing and Gaussian distribution. The local colonic surface was classified as polyp or fold using a feature normalized nearest neighborhood classifier. The main merit of this paper is the methodology applied to select the robust features derived from the colon surface that have a high discriminative power for polyp/fold classification. The devised polyp detection scheme entails a low computational overhead (typically takes 2.20 min per dataset) and shows 100% sensitivity for phantom polyps greater than 5 mm. It also shows 100% sensitivity for real polyps larger than 10 mm and 91.67% sensitivity for polyps between 5 to 10 mm with an average of 4.5 false positives per dataset. The experimental data indicates that the proposed CAD polyp detection scheme outperforms other techniques that identify the polyps using features that sample the colon surface curvature especially when applied to low-dose datasets.     

结外淋巴瘤:影像学共性特征与病理的关系

目的 分析结外淋巴瘤的影像学特征,探讨影像学对其诊断价值.资料与方法 回顾性分析57例经手术病理证实的结外淋巴瘤病例,非霍奇金病54例,霍奇金病3例,初诊时均无明确淋巴瘤病史,术前分别经常规X线、CT和MRI检查.结果 57例结外淋巴瘤中,消化道14例,骨骼9例,脑7例,肾5例,肺4例,脾4例,肾上腺4例,肝脏3例,肌肉3例,胰腺2例,睾丸1例,阴道1例.常规X线检查22例,术前诊断6例;CT检查39例,术前诊断17例;MRI检查19例,术前诊断12例.CT检查47个病灶中,41个病灶密度均匀,6个病灶有程度不等坏死.MRI检查26个病灶中,T1WI为低或等信号,25个病灶T2WI为低、等或略高信号,1个病灶为高信号;17个病灶信号均匀,9个病灶信号不均匀.动态CT和MR增强47例,除脑淋巴瘤为进行性显著持续强化外,其他部位淋巴瘤为进行性轻到中度延迟强化.结外淋巴瘤瘤内常可见脏器原有解剖结构,如血管、支气管、肾盂、肌间隙等残留.结论 结外淋巴瘤共性表现包括肿瘤密实,瘤内可见原有解剖结构残留,MR T2WI多为等或略高信号;一般轻度至中度延迟强化.掌握结外淋巴瘤的一些特征性影像学表现,有利于提高诊断准确率.     

基于多生理信号的情绪初步识别

以生理信号分析为主,表情行为观察和情绪主观感受评价为辅,对多名被试的情绪进行识别.60名大学女生接受恐惧-快乐-轻松的情绪诱发,有效数据55名,对应每个情绪片段,根据信号标记以及GSR微分,截取1min的生理信号进行处理和分析,应用SPSS对各生理参数进行情绪的单因素方差分析,然后采用逐步多类判别法,提取特征参数以识别情绪.结果表明HR、HRV、R波、T波各生理参数对情绪较敏感;提取出HFP,HR max,PNN50,LF/HF,Ratio,LFP,Mean NN 7个特征参数,构建情绪判别函数Fuction1,Fuction2和Z1、Z2,Z3;轻松的判别正确率为88.0%,快乐的为92.0%,恐惧的为80.0%,总体判别正确率为86.7%.以生理信号分析为主,辅助表情行为观察和情绪主观感受报告,是一种有效的情绪识别方法,所得数据客观、准确,提高了情绪识别率.     

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。     

An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks

In this paper, an intelligent system is presented for interpretation of the Doppler signals of the heart valve diseases based on the pattern recognition. This paper especially deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler Ultrasound. Because of this, a wavelet packet neural network model developed by us is used. The model consists of two layers: wavelet and multi-layer perceptron. The wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of wavelet packet decomposition and wavelet packet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the developed system has been evaluated in 215 samples. The test results showed that this system was effective in detecting Doppler heart sounds. The correct classification rate was about 94% for abnormal and normal subjects.     

基于自适应小波神经网络的心电图检测法

自适应小波神经网络检测法就是利用小波函数取代通常神经网络中隐层的作用函数来实现的，通过网络的学习自适应地调整尺度参数和时移因子，提高了特征提取能力，因此，该网络可提高心电信号的检测率和可靠性。     

基于经验模式分解的心电特征提取算法

本研究应用基于经验模式分解的心电特征提取方法,利用第一本征模函数(intrinsic mode function,IMF)分量对QRS波进行定位,并通过减少分解层数、筛选次数、处理区域等策略实现了快速算法。利用MIT-BIT心律失常数据库的数据进行算法测试,取得较高的检测率,检测速度也有明显提高。实验结果表明,经验模式分解算法在QRS波定位中具有相当的优越性,临床应用中取得了良好的检测效果。     

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。     

引入关联维的表面肌电信号的特征提取

考虑到表面肌电信号的非平稳特性,本研究在传统特征提取的基础上,又引入了非线性动力学中的关联维,通过小波系数的标准差和关联维重新构造特征向量,将其送入自组织映射网络对拇指弯曲、食指弯曲和无名指弯曲三种手势动作进行分类识别。结果表明:引入关联维的特征提取方法其识别正确率明显优于传统的小波变换的方法。可见,关联维作为一种新的特征参数,为肌电信号的特征提取提供了新的思路。