抑郁症患者脑电复杂度的小波熵分析

应用小波熵理论分析抑郁症患者和健康人在安静和心算任务下自发脑电信号的复杂度：分别采集10例抑郁症患者和10例正常人在安静闭目和闭眼心算连减两种状态下的16导联脑电信号;计算这四组脑电数据的小波熵,并进行对比和统计分析。结果表明,抑郁症患者和正常人自发脑电的小波熵有着显著的差异：（1）在相同状态下,抑郁症患者各导联脑电的小波熵大于正常人对应导联的小波熵;（2）对同一个人,安静闭目状态下各导联脑电的小波熵大于心算连减状态下对应导联的小波熵。结论可为抑郁症的诊断提供参考。     

A novel method for the classification of Alzheimer's disease from normal controls using magnetic resonance imaging

Alzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD. The main motive of this paper is to improve the classification and prediction accuracy of AD. In this paper, a novel approach is developed to classify MCI, normal control (NC), and AD using structural magnetic resonance imaging (sMRI) from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset (50 AD, 50 NC, 50 MCI subjects). FreeSurfer is used to process these MRI data and obtain cortical features such as volume, surface area, thickness, white matter (WM), and intrinsic curvature of the brain regions. These features are modified by normalizing each cortical region's features using the absolute maximum value of that region's features from all subjects in each group of MCI, NC, and AD independently. A total of 420 features are obtained. To address the curse of dimensionality, the obtained features are reduced to 30 features using a sequential feature selection technique. Three classifiers, namely the twin support vector machine (TSVM), least squares TSVM (LSTSVM), and robust energy‐based least squares TSVM (RELS‐TSVM), are used to evaluate the classification accuracy from the obtained features. Five‐fold and 10‐fold cross‐validation are used to validate the proposed method. Experimental results show an accuracy of 100% for the studied database. The proposed approach is innovative due to its higher classification accuracy compared to methods in the existing literature.     

Using a virtual environment to assess cognition in the elderly

Early diagnosis of Alzheimer’s disease (AD) is essential if treatments are to be administered at an earlier point in time before neurons degenerate to a stage beyond repair. In order for early detection to occur tools used to detect the disorder must be sensitive to the earliest of cognitive impairments. Virtual reality technology offers opportunities to provide products which attempt to mimic daily life situations, as much as is possible, within the computational environment. This may be useful for the detection of cognitive difficulties. We develop a virtual simulation designed to assess visuospatial memory in order to investigate cognitive function in a group of healthy elderly participants and those with a mild cognitive impairment (MCI). Participants were required to guide themselves along a virtual path to reach a virtual destination which they were required to remember. The preliminary results indicate that this virtual simulation has the potential to be used for detection of early AD since significant correlations of scores on the virtual environment with existing neuropsychological tests were found. Furthermore, the test discriminated between healthy elderly participants and those with a MCI.     

基于特征融合方法的轻微认知衰退静息态脑电数据自动检测技术研究

轻微认知衰退是阿尔茨海默病的早期阶段,而利用脑电信号进行轻微认知衰退的特征提取与分类是诊断轻微认知衰退的重要方法。在基于脑电人工智能轻微认知衰退自动检测技术中,现有研究只提取脑电波信号中的某一个特征或简单地拼接多个特征,这会导致这些方法并不能较好地考虑特征之间的相关性,并且会引发维度灾难的问题;提出了一种基于卷积神经网络的轻微认知衰退静息态脑电数据自动检测算法,通过提取脑电的功率谱及脑网络特征,并通过矩阵运算的方式对这两种特征进行融合,利用卷积神经网络对融合后的特征进行分类。该方法在上海某医院采集的数据集上获得较高的准确率;此外,通过输入特征集的不同子集,该方法找到了对轻微认知衰退最有贡献的几组特征,从而还具有一定的可解释性。在本数据集上证明了功率脑网络对于轻微认知衰退自动诊断的优势。     

F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis

Finding sensitive and appropriate technologies for non-invasive observation and early detection of Alzheimer’s disease (AD) is of fundamental importance to develop early treatments. In this work we develop a fully automatic computer aided diagnosis (CAD) system for high-dimensional pattern classification of baseline ¹⁸F-FDG PET scans from Alzheimer’s disease neuroimaging initiative (ADNI) participants. Image projection as feature space dimension reduction technique is combined with an eigenimage based decomposition for feature extraction, and support vector machine (SVM) is used to manage the classification task. A two folded objective is achieved by reaching relevant classification performance complemented with an image analysis support for final decision making. A 88.24% accuracy in identifying mild AD, with 88.64% specificity, and 87.70% sensitivity is obtained. This method also allows the identification of characteristic AD patterns in mild cognitive impairment (MCI) subjects.     

用于阿尔茨海默病诊断的权值分布特征学习

针对当前基于机器学习的早期阿尔茨海默病（AD）诊断中有标记训练样本不足的问题,提出一种基于多模态特征数据的权值分布稀疏特征学习方法,并将其应用于早期阿尔茨海默病的诊断.具体来说,该诊断方法主要包括两大模块：基于权值分布的Lasso特征选择模型（WDL）和大间隔分布分类机模型（LDM）.首先,为了获取多模态特征之间的数据分布信息,对传统Lasso模型进行改进,引入权值分布正则化项,从而构建出基于权值分布的Lasso特征选择模型;然后,为了有效地利用多模态特征之间的数据分布信息,以保持多模态特征之间的互补性,直接采用大间隔分布学习算法训练分类器.选取国际阿尔茨海默症数据库（ADNI）中202个多模态特征的被试者样本进行实验,分类AD最高平均精度为97.5%,分类轻度认知功能障碍（MCI）最高平均精度为83.1%,分类轻度认知功能障碍转化为AD（pMCI）最高平均精度为84.8%.实验结果表明,所提WDL特征学习方法可从串联的多模态特征学到性能更优的特征子集,并能根据权值分布获取多模态特征之间的数据分布信息,从而提高早期阿尔茨海默病诊断的性能.     

基于认知任务的脑机接口方法研究

提出一种通过脑电波来识别放松状态以及乘法作业状态从而实现脑机接口的新方法。利用脑电仪记录受测者放松状态以及乘法作业时的大脑左右半球枕叶部的脑电信号，采用Welch法分别估计出这2个部位8Hz~10Hz、1Hz~13Hz、14Hz~30Hz 3个频段的功率谱，以各个功率谱平均值和2Hz~30Hz频段功率谱平均值的比值作为分类特征，采用支持向量机的方法建立了分类器，从而实现了脑机接口。4个受测者的实验结果表明识别准确率都大于94.44%，最高为98.89%。由于只采用了2个采集点，因此如果采用某种编码方式，该脑机接口技术就可更加方便地用于写字、控制轮椅等方面。     

Hyperspectral anomaly detection using spectral–spatial features based on the human visual system

ABSTRACT

Anomaly detection (AD) is one of the most attracting topics within the recent 10 years in hyperspectral imagery (HSI). The goal of the AD is to label the pixels with significant spectral or spatial differences to their neighbours, as targets. In this paper, we propose a method that uses both spectral and spatial information of HSI based on human visual system (HVS). By inspiring the retina and the visual cortex functionality, the multiscale multiresolution analysis is applied to some principal components of hyperspectral data, to extract features from different spatial levels of the image. Then the global and local relations between features are considered based on inspiring the visual attention mechanism and inferotemporal (IT) part of the visual cortex. The effects of the attention mechanism are implemented using the logarithmic function which well highlights, small variations in pixels’ grey levels in global features. Also, the maximum operation is used over the local features for imitating the function of IT. Finally, the information theory concept is used for generating the final detection map by weighting the global and local detection maps to obtain the final anomaly map. The result of the proposed method is compared with some state-of-the-art methods such as SSRAD, FLD, PCA, RX, KPCA, and AED for two well-known real hyperspectral data which are San Diego airport and Pavia city, and a synthetic hyperspectral data. The results demonstrate that the proposed method effectively improves the AD capabilities, such as enhancement of the detection rate, reducing the false alarm rate and the computation complexity.     

Ontology driven decision support for the diagnosis of mild cognitive impairment

In recent years, mild cognitive impairment (MCI) has attracted significant attention as an indicator of high risk for Alzheimer's disease (AD), and the diagnosis of MCI can alert patient to carry out appropriate strategies to prevent AD. To avoid subjectivity in diagnosis, we propose an ontology driven decision support method which is an automated procedure for diagnosing MCI through magnetic resonance imaging (MRI). In this approach, we encode specialized MRI knowledge into an ontology and construct a rule set using machine learning algorithms. Then we apply these two parts in conjunction with reasoning engine to automatically distinguish MCI patients from normal controls (NC). The rule set is trained by MRI data of 187 MCI patients and 177 normal controls selected from Alzheimer's Disease Neuroimaging Initiative (ADNI) using C4.5 algorithm. By using a 10-fold cross validation, we prove that the performance of C4.5 with 80.2% sensitivity is better than other algorithms, such as support vector machine (SVM), Bayesian network (BN) and back propagation (BP) neural networks, and C4.5 is suitable for the construction of reasoning rules. Meanwhile, the evaluation results suggest that our approach would be useful to assist physicians efficiently in real clinical diagnosis for the disease of MCI.     

结合DCGAN与LSTM的阿兹海默症分类算法

针对传统的阿兹海默症（AD）分类3D模型参数过多以及2D模型缺乏连续性特征的问题,提出了一种结合2D卷积神经网络与长短时记忆网络的脑部核磁共振成像（MRI）图像分类算法。利用深度卷积生成对抗网络（DCGAN）,卷积层能够在无标签的情况下自动提取到图像特征。首先以无监督的方式训练卷积神经网络;然后将MRI图像序列转换为特征序列,再输入到长短时记忆网络进行训练;最后结合特征序列与LSTM的隐藏状态进行分类。实验结果显示,相比3D模型,该算法有着更少的参数,对于NC与AD的分类达到了93.93%的准确率,对于NC与MCI的分类达到了86.27%的准确率。     

Early diagnosis model of Alzheimer’s Disease based on sparse logistic regression

Multimedia Tools and Applications - Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI) are critical for the effective treatment...     

Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG

Predicting the onset of epileptic seizure is an important and difficult biomedical problem, which has attracted substantial attention of the intelligent computing community over the past two decades. We apply recurrent neural networks (RNN) combined with signal wavelet decomposition to the problem. We input raw EEG and its wavelet-decomposed subbands into RNN training/testing, as opposed to specific signal features extracted from EEG. To the best of our knowledge this approach has never been attempted before. The data used included both scalp and intracranial EEG recordings obtained from two epileptic patients. We demonstrate that the existence of a “preictal” stage (immediately preceding seizure) of some minutes duration is quite feasible.     

基于HHT的脑电信号在不同阅读模式下的识别与分类

目的：针对Powerlab脑电信号记录仪获取脑电波形,分辨不同类型的脑电阅读模式。方法：对实验者在阅读不同材料（平静闭目、阅读英语、阅读诗歌、阅读现代文四种阅读模式）时的头皮脑电信号进行采样,使用希尔伯特-黄变换及支持向量机训练,分辨平静闭目和其他三种不同阅读模式,并针对经验模态分解时出现的常见情况——端点飞翼现象进行算法优化处理并比较其处理效果。结果：基于多项式拟合处理的经验模态分解分解后的脑电信号辨识率最高,稳定在65%水平,最高可达70%。结论：大脑在阅读状态下经经验模态分解和多项式拟合后的信号适合作为大脑阅读模式下的特征提取函数,并对有效阅读模式具有指导意义。     

Re-transfer learning and multi-modal learning assisted early diagnosis of Alzheimer’s disease

Nowadays more and more elderly people are suffering from Alzheimer’s disease (AD). Finely recognizing mild cognitive impairment (MCI) in early stage of the symptom is vital for AD therapy. However, brain image samples are relatively scarce, meanwhile have multiple modalities, which makes finely classifying brain images by computers extremely difficult. This paper proposes a fine-grained brain image classification approach for diagnosing Alzheimer’s disease, with re-transfer learning and multi-modal learning. First of all, an end-to-end deep neural network classifier CNN4AD is designed to finely classify diffusion tensor image (DTI) into four categories. And according to the characteristics of multi-modal brain image dataset, the re-transfer learning method is proposed based on transfer learning and multi-modal learning theories. Experimental results show that the proposed approach obtain higher accuracy with less labeled training samples. This could help doctors diagnose Alzheimer’s disease more timely and accurately.

     

Identification of differential brain regions in MCI progression via clustering-evolutionary weighted SVM ensemble algorithm

Mild cognitive impairment (MCI) as the potential sign of serious cognitive decline could be divided into two stages, i.e., late MCI (LMCI) and early MCI (EMCI). Although the different cognitive states in the MCI progression have been clinically defined, effective and accurate identification of differences in neuroimaging data between these stages still needs to be further studied. In this paper, a new method of clustering-evolutionary weighted support vector machine ensemble (CEWSVME) is presented to investigate the alterations from cognitively normal (CN) to EMCI to LMCI. The CEWSVME mainly includes two steps. The first step is to build multiple SVM classifiers by randomly selecting samples and features. The second step is to introduce the idea of clustering evolution to eliminate inefficient and highly similar SVMs, thereby improving the final classification performances. Additionally, we extracted the optimal features to detect the differential brain regions in MCI progression, and confirmed that these differential brain regions changed dynamically with the development of MCI. More exactly, this study found that some brain regions only have durative effects on MCI progression, such as parahippocampal gyrus, posterior cingulate gyrus and amygdala, while the superior temporal gyrus and the middle temporal gyrus have periodic effects on the progression. Our work contributes to understanding the pathogenesis of MCI and provide the guidance for its timely diagnosis.     

Identifying stage of Alzheimer disease using multiclass particle swarm optimisation technique

ABSTRACT

Detecting brain structural changes from magnetic resonance (MR) images can facilitate early diagnosis and treatment of neurological and psychiatric diseases. Alzheimer Disease (AD) is a progressive neurodegenerative disorder that causes structural changes in patient’s brain. As such, it is essential to develop an algorithm for identifying the biomarkers of this disease stage. We developed a novel volumetric analysis of anatomical components of brain with multiclass particle swam optimisation technique (MPSO) approach to detect the stages of AD as potential biomarkers. To avoid image distortion bias correction is applied. We have used anatomical structures i.e. tissue and ventricle volume are used as criteria to categorise image features into four classes such as Alzheimer Mild cognitive decline, Alzheimer Moderate Cognitive decline and Alzheimer Severe Cognitive decline and healthy subject. This work was experimented with 30 AD and 10 normal cases. We observed that grey matter content was reduced from 4 to 20% of normal brain and volume of ventricle is increasing gradually from mild to severe cognitive decline. The statistical performance measures are calculated for proposed and existing work. The value shows that our empirical evaluation has superior diagnosis performance. We found that AD patient’s brain has reduced volume in grey matter and subsequently shrunk the volume of brain. The size of ventricle is also the major concern to predict the severity of AD disease. Therefore, the volumes of grey matter and ventricle size more discriminately classify the AD patient with severity from normal subject.     

Relationship between tau,neuroinflammation and atrophy in Alzheimer's disease: The NIMROD study

In addition to beta-amyloid accumulation, misfolded tau and activated microglia are also present in Alzheimer's disease (AD). It is important to study the relationship amongst these pathologies in vivo and their effects on the cognitive deficits for developing effective trails and future therapeutic or preventive strategies for AD. To investigate the relationships amongst different pathologies in AD, in particular how they interact resulting in cognitive impairments, we conducted a study of sixty-six subjects (15 AD, 24 Mild Cognitive Impairment (MCI) and 27 similarly aged healthy controls), who underwent standardised clinical and neuropsychological assessments followed by dynamic PET using [¹⁸F]AV1451 (tau) and [¹¹C]PK11195 (activated microglia) and multimodal 3T MRI. MCI patients also underwent [¹¹C]PIB (beta-amyloid) PET. We compared regional PET binding and grey matter atrophy amongst AD, amyloid positive MCI and controls, as well as their spatial distribution across different brain areas. We also applied a mediation analysis to infer the direct and indirect effects of tau, neuroinflammation and grey matter atrophy on cognitive functioning. We found increased [¹⁸F]AV1451 and [¹¹C]PK11195 binding as well as grey matter atrophy in AD, with a strong spatial overlap amongst these AD related biomarkers suggesting them interacting with each other. We demonstrated that both tau ([¹⁸F]AV1451) and neuroinflammation ([¹¹C]PK11195) have significant effects on cognition however their effects were fully mediated by grey matter atrophy. No mediation effect between tau and neuroinflammation were found with respect to cognition. In conclusion, grey matter atrophy not only spatially overlapped with tau and microglia activity in AD, but also mediate them in affecting cognitive impairments. The mediation analysis enabled data fusion across multiple imaging modalities (PET and MRI) and multiple PET tracers. Our results have significant implications for trials targeting tau and inflammation, and future therapeutic or preventive strategies for AD.     

Classification among healthy,mild cognitive impairment and Alzheimer’s disease subjects based on wavelet entropy and relative beta and theta power

Pattern Analysis and Applications - Diagnosis of Alzheimer’s disease (AD), mild cognitive impairment (MCI), and healthy subjects (Healthy) is currently lacking an automated tool. It requires...