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.     

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。     

Responses of the human auditory cortex to changes in one versus two stimulus features

Neuromagnetic responses were recorded with a 24 SQUID magnetometer in two oddball experiments to determine whether mismatch responses to changes in single stimulus features are additive. In experiment 1, the one feature deviants differed from standards in interstimulus interval (ISI) or frequency, and the two feature deviants in both ISI and frequence. In experiment 2, deviants differed in duration, frequency, or both. All deviants evoked a mismatch field (MMF) with sources close to each other in the supratemporal auditory cortex. Except for the ISI deviants, the MMF sources were about 1 cm anterior to the source of the 100ms response, N100m, to the standards. In the two experiments, MMFs obtained in response to the two feature deviants resembled closely the sum of MMFs in response to one feature deviants. The results suggest that the standards leave a multiple neuronal representation in the human auditory cortex. The particular neuronal traces of the representation react independently to changes in different features of sound stimuli.     

青年人急性心肌梗死的特点及预防

目的：探讨青年人急性心肌梗死的特点及预防。方法：对28例青年人（≤40岁）心肌梗死的临床资料进行分析。结果：本组男性26例，占92．9％；7-9月份发病16例，占57％；体力劳动者22例，占78．6％；有明显冠心病危险因素者28例，占89．3％；发病前有诱发因素者23例，占82．1％。结论：青年人心肌梗死具有男性多见、夏季多见、体力劳动者多见、危险因素多明显、诱发因素多突出、先兆少、症状多典型等特点。其发生多与不良生活方式有关。加强健康教育，建立良好的生活方式是预防青年人心肌梗死的关键。