Discourse Processing in Attention-Deficit Hyperactivity Disorder (ADHD)

ADHD is a psychiatric disorder characterised by persistent and developmentally inappropriate levels of inattention, impulsivity and hyperactivity. It is known that children with ADHD tend to produce incoherent discourses, e.g. by narrating events out of sequence. Here the aetiology of ADHD becomes of interest. One prominent theory is that ADHD is an executive function disorder, showing deficiencies of planning. Given the close link between planning, verb tense and discourse coherence postulated in van Lambalgen and Hamm (The proper treatment of events, 2004), we predicted specific deviations in the verb tenses produced by children with ADHD. Here we report on an experiment corroborating these predictions. This article is based on the theoretical analysis of executive function given by Stenning and van Lambalgen (2007). The analysis of the narratives of the children with ADHD is due to the second and third authors. The application to tense processing owes much to conversations with Giosue Baggio     

基于时空双稀疏表示的成人ADHD脑网络检测与分析

注意力缺陷多动障碍（Attention deficit hyperactivity disorder，ADHD）主要表现为注意力分散、多动和冲动，是一种常见的精神障碍疾病.作为一种流行的脑功能成像技术，静息态功能核磁共振成像（Resting-state functional magnetic resonance imaging，rsfMRI）常应用于探索ADHD的神经机制.然而，由于rsfMRI数据的高维和少样本特性，采用传统的独立成分分析方法从rsfMRI数据中获得脑网络后，大多用基于体素级的方法进行推断，这难以检测出可靠的、与ADHD相关的脑网络.针对上述问题，本文提出了一种新颖的基于时空双稀疏表示（Dual temporal and spatial sparse representation，DTSSR）的方法和指标，以22名成人ADHD患者为研究对象，从大尺度脑网络级的角度检测出与ADHD相关的脑网络.首先采用DTSSR从ADHD的rsfMRI数据中提取出组脑网络及相应的耦合参数；然后将耦合参数均值池化作为网络的活跃度指标；最后，将活跃度指标与ADHD的量表分进行Spearman相关性分析，检测出与ADHD相关的脑网络.实验结果表明，背侧注意网络、执行控制网络的活跃度与ADHD量表分具有显著相关性.该结果在脑科学角度有合理的解释，且在不同字典尺寸下具有较高稳定性.本文所提方法，为探讨ADHD的潜在神经机制提供了一种新思路.     

Stockwell transform of time-series of fMRI data for diagnoses of attention deficit hyperactive disorder

Attention deficit hyperactivity disorder (ADHD) is a common brain disorder among children. It presents various symptoms, hence, utilizing the information obtained from functional magnetic resonance imaging (fMRI) time-series data can be useful. Finding functional connections in typically developed control (TDC) and ADHD patients can be helpful in classification. The aim of this paper is to present a multifold method for the study of fMRI data to diagnose ADHD patients. In the proposed method, first, by applying the Stockwell transform (ST), we obtain detailed information about the time-series of the region of interests (ROIs) in the time and frequency domains. ST provides information about the variations of each ROI during the time. Thereafter, time-frequency domains are partitioned into sub-matrices and then, their fuzzy entropies are calculated as features. Next, discriminative features are chosen by using the two-sample Kolmogorov–Smirnov (K–S) test. Finally, the data are classified by the leave-one-out cross-validation (LOOCV) method using the support vector machine (SVM) classifier. To see the effectiveness of the proposed method, the experiments are performed on the ADHD-200 database. We consider different scenarios including classification of TDCs and ADHDs as well as classification of ADHD subtypes. We also assess the performance by considering the age and sex as phenotypic information. The proposed method gives good results in the classification procedure and identifying the connection paths between ROIs. The results indicate that the proposed method can distinguish ADHD disorder in a more accurate manner in comparison with other methods. The connectivity paths show that there is a reduction in the input of cerebellar regions and the left mid orbitofrontal cortex in ADHDs compared to TDCs.     

Always Connected or Always Distracted? ADHD Symptoms and Social Assurance Explain Problematic Use of Mobile Phone and Multicommunicating

Multicommunicating via mobile phone during face‐to‐face encounters with family and friends was examined with data from an online survey of 432 adults in the US. Multicommunicating was positively associated with problematic mobile phone use and explained by attention deficit/hyperactivity disorder (ADHD) symptoms and a social need to belong. Those with ADHD symptoms were more likely to engage in problematic mobile phone use and frequent multicommunicating. Strong need for social assurance, which involves an “always‐on‐and‐connected” lifestyle through mobile devices, explained problematic mobile phone use and multicommunicating. Need for social assurance also was related to multicommunicating via social connectedness, and this effect was significant among females, but not males. The implications of these findings for future research are discussed.     

基于皮尔逊最优电极选择的ADHD患者脑电特征提取及分类研究

事件相关电位(ERP)可用于注意缺陷多动障碍儿童(ADHD)和正常儿童的脑电特征 提取与分类。首先,采用赌博任务范式,采集2 类儿童的脑电信号;其次,基于皮尔逊相关系 数算法选择最优电极,并预处理最优电极脑电信号;然后,提取预处理脑电信号的时域特征(均 值、方差、峰值)和频域特征(Theta 波段功率、Alpha 波段功率);最后,利用传统分类方法支持 向量机(SVM)、自适应增强(AdaBoost)、自举汇聚法(Bagging)、线性判别式分析(LDA)、反向传 播(BP)和组合分类器的分类方法(LDA-SVM,BP-SVM)完成对2 种脑电信号的分类。研究结果 表明,传统方法BP 分类器的分类准确率可达80.52%,组合分类器BP-SVM 的分类准确率可达 88.88%。组合分类方法能提高ADHD 儿童的分类准确率,为基于脑机接口技术的ADHD 神经 反馈康复治疗提供技术支持。     

A secure mHealth application for attention deficit and hyperactivity disorder

Nowadays, many people have smartphones, the fact that encourages the development of new tools to address different problems. One of its consequences is the recent growth of mHealth, a term that refers to the practice of medicine based on the use of mobile devices for medical and health purposes. This work describes a new mHealth tool to improve memory and cognitive abilities through gamification and serious games. In particular, a mobile application here is proposed to help children that suffer from attention deficit hyperactivity disorder (ADHD). This application integrates the four profiles involved in this disorder: children, parents, teachers, and medical staff. With it, parents can discover if their children suffer the disorder, and children can improve their cognitive abilities through games of different types. Besides, the security aspect of the proposal is emphasized to highlight its importance in mHealth. Thus, the developed tool includes various cryptographic mechanisms to protect the confidentiality of communications and the authenticity of users and data.     

基于语音停顿度和平坦度的注意缺陷与多动障碍自动检测算法

针对注意缺陷与多动障碍（ADHD）临床诊断主要依靠医生主观评估,缺乏客观辅助依据的问题,提出了一种基于语音停顿度和平坦度的ADHD自动检测算法。首先,通过频带差能熵积（FDEEP）参数自动定位语音有话区间,并提取停顿度特征;然后,使用变换平均幅度平方差（TAASD）参数计算语音倍频率,并提取平坦度特征;最后,结合融合特征和支持向量机（SVM）分类器来实现ADHD的自动识别。实验共采集了17位正常对照组儿童和37位ADHD患儿的语音样本。实验结果表明,所提算法能自动检测正常儿童和ADHD患儿,识别正确率为91.38%。     

Statistical Analysis and Multimodal Classification on Noisy Eye Tracker and Application Log Data of Children with Autism and ADHD

Emotion recognition behavior and performance may vary between people with major neurodevelopmental disorders such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and control groups. It is crucial to identify these differences for early diagnosis and individual treatment purposes. This study represents a methodology by using statistical data analysis and machine learning to provide help to psychiatrists and therapists on the diagnosis and individualized treatment of participants with ASD and ADHD. In this paper we propose an emotion recognition experiment environment and collect eye tracker fixation data together with the application log data (APL). In order to detect the diagnosis of the participant we used classification algorithms with the Tomek links noise removing method. The highest classification accuracy results were reported as 86.36% for ASD vs. Control, 81.82% for ADHD vs. Control and 70.83% for ASD vs. ADHD. This study provides evidence that fixation and APL data have distinguishing features for the diagnosis of ASD and ADHD.     

Classification of BMD and ADHD patients using their EEG signals

Bipolar Mood Disorder (BMD) and Attention Deficit Hyperactivity Disorder (ADHD) patients mostly share clinical signs and symptoms in children; therefore, accurate distinction of these two mental disorders is a challenging issue among the psychiatric society. In this study, 43 subjects are participated including 21 patients with ADHD and 22 subjects with BMD. Their electroencephalogram (EEG) signals are recorded by 22 electrodes in two eyes-open and eyes-closed resting conditions. After a preprocessing step, several features such as band power, fractal dimension, AR model coefficients and wavelet coefficients are extracted from recorded signals. This paper is aimed to achieve a high classification rate between ADHD and BMD patients using a suitable classifier to their EEG features. In this way, we consider a piece wise linear classifier which is designed based on XCSF. Experimental results of XCSF-LDA showed a significant improvement (86.44% accuracy) compare to that of standard XCSF (78.55%). To have a fair comparison, the other state-of-art classifiers such as LDA, Direct LDA, boosted JD-LDA (BJDLDA), and XCSF are assessed with the same feature set that finally the proposed method provided a better results in comparison with the other rival classifiers. To show the robustness of our method, additive white noise with different amplitude is added to the raw signals but the results achieved by the proposed classifier empirically confirmed a higher robustness against noise compare to the other classifiers. Consequently, the proposed classifier can be considered as an effective method to classify EEG features of BMD and ADHD patients.     

Risk factors associated with online game addiction: A hierarchical model

Online gaming addiction has been increasingly recognized as a mental disorder. However, the predictive factors that lead to online gaming addiction are not well established. The aim of this study was to identify factors that may influence the development of online gaming addiction. A total of 263 patients with problematic online gaming addiction (255 males (97%) and 8 females (3%), age: mean = 20.4 ± 5.8 years) and153 healthy comparison subjects (118 males (77%) and 35 females (23%), age: 21.2 ± 5.5 years, range) were recruited for participation in the current study. Hierarchical logistic regression analyses among each set of variables were conducted. Individual factors (sex and age), cognitive factors (IQ and perseverative errors), psychopathological conditions (ADHD, depression, anxiety, and impulsivity), and social interaction factors (family environment, social anxiety, and self-esteem) were evaluated in a stepwise fashion. All four factors were associated with online gaming addiction, with psychopathological conditions being the strongest risk factors for the addiction. Individual factors, psychological factors, and social interactions were associated with the development of pure online gaming addiction. As before, psychological factors (attention, mood, anxiety and impulsivity) were the strongest risk factors for online gaming addiction in patients with pure online gaming addiction. Psychopathologies, including ADHD and depression, were the strongest factors associated with the development of online gaming addiction in individuals.     

Fairy tale directed game-based training system for children with ADHD using BCI and motion sensing technologies

We developed a game-based training system to analyse and improve the reading ability of children with Attention Deficit/Hyperactivity Disorder (ADHD). A fairy tale-based interactive narrative is used as an intervention strategy in the behaviour training system, which collects brainwaves and motion-sensing data during treatment. The system includes fairy tales as well as attention and behaviour-related tasks coupled with a brain-computer interface (BCI) and motion-sensing technology. The treatment for the children (N?=?5) diagnosed with ADHD was performed for five weeks on a weekly basis, comprised of one 20-minute long adaptation session and four 40-minute long sessions. For the quantitative analysis of the treatment, pre- and post- KNISE-BAAT and general reading questionnaires were administered. Sensing data was also recorded. In-depth post-interviews were conducted after the completion of the treatment programme for qualitative analysis. The paired-samples t-test on both reading comprehension tests indicate improvement in both reading aloud and reading comprehension. The sensing data analysis shows improvements in attention span and decreases in hyperactive behaviour over time. The analysis on the interview data supports the quantitative test results. As such, the test results indicate that this approach helps children with ADHD improve their reading ability, increase their attention span, and support behavioural inhibition.     

脑电生物反馈仪的设计与实现

用脑电生物反馈治疗儿童注意力障碍多动综合症，是一种强有力的手段。本文设计了功能齐全、性能优良的脑电生物反馈系统，提出了系统硬件和软件的实现方案，并研制了该系统的样机，具有一定的临床应用价值。     

面向ADHD的利用HTC Vive采集手部运动数据的可行性研究

虚拟现实技术在辅助注意力缺陷伴多动障碍(ADHD)的客观诊断中取得了一定成效;为了分析ADHD患者与正常儿童在虚拟环境中手部交互的运动差异,需要追踪被试的手部运动并对其进行测量;消费级虚拟现实系统HTC Vive配有手持控制器,具有运动捕捉功能;然而目前临床上还未将手持控制器用于人体手部运动追踪,因此尝试探讨手持控制器用于追踪人体手部运动的可行性,并设计了两个实验;实验一评估HTC Vive系统追踪手持控制器静置时的位置和旋转精度,分析系统的随机误差;实验二评估系统追踪手持控制器的平移和旋转精度;实验一测量得出:手持控制器静置时在X、Y、Z轴的位置抖动误差均小于0.25 mm,旋转抖动误差均小于0.05°;进而采用艾伦方差方法对实测数据进行分析,得到系统量化噪声、零偏不稳定性、速率斜坡等主要误差项系数;实验二测量得出:手持控制器绕X、Y、Z轴的旋转误差均小于0.35°,沿X、Y、Z轴的平移误差均小于3 mm;实验结果表明:HTC Vive系统可用于采集人体的手部运动数据,为辅助ADHD患者的客观诊断提供了可靠的基础支持。     

Disability digital divide: the use of the internet,smartphones, computers and tablets among people with disabilities in Sweden

Although Sweden is one of the most digitalized countries and the Swedish population’s use of the internet is among the most studied in the world, little is known about how Swedes with disabilities use internet. The purpose of this study is to describe use of and perceived difficulties in use of the internet among people with disabilities and to explore digital divides in-between and within disability groups, and in comparison with the general population. This is a cross-sectional survey targeting the same issues as other nationwide surveys but adapted for people with cognitive disabilities. Participants were recruited from May to October 2017 by adaptive snowball sampling. The survey comprised questions on access to and use of devices, and use of and perceived difficulties in use of internet. A total of 771 people responded to the survey, representing 35 diagnoses/impairments. Larger proportions of people with autism, ADHD and bipolar disorder reported using internet than other disability groups. Women with autism used the internet more than any other disability group, and women with aphasia used the internet the least. People with disabilities related to language and understanding reported more difficulties using internet than other disability groups. Larger proportions of participants than the general Swedish population reported not feeling digitally included. In many but not all disability groups, larger proportions of men than women reported not feeling digitally included. Our findings show that there are differences in digital inclusion between sub-groups of diagnoses/impairments. Thus, disability digital divides are preferably investigated by sub-grouping disabilities, rather than studied as one homogeneous group.

     

Effects of computer collaborative group work on peer acceptance of a junior pupil with attention deficit hyperactivity disorder (ADHD)

The study investigated the effects of computer collaborative group work, facilitated by an adult, on peer acceptance of a junior boy with Attention Deficit Hyperactivity Disorder (ADHD). It aimed to ascertain whether collaborative group work on a computer, with the facilitation of an adult, could help to raise his peer acceptance among his classmates. Data were gathered before and after computer group work sessions. Interviews and questionnaires were conducted on parents and teachers to help establish the history background data and their concerns on the boy’s peer acceptance. Sociometrics testing of classmates was used to determine peer acceptance among the boy’s immediate peer group. During the adult-facilitated computer sessions, the facilitator reinforced positive social behaviour and interactions. After the computer sessions, interviews and questionnaires were conducted again on parents, teachers and facilitator in an attempt to capture information on the child’s behaviour and attitudes during the computer sessions. Sociometrics testing was also conducted again to determine if the peer acceptance among the boy’s immediate peer group has changed. The results indicated encouraging improvements in the raise of his peer acceptance among his classmates in general. Although this was a discrete setting, the finding is promising and this strategy may be replicated in school to support mainstream inclusion for children with ADHD.     

试谈解决JSP Web开发中的中文乱码

介绍在JSP中解决页面显示的中文乱码、URL传递参数、数据提交后的中文乱码以及访问MySQL数据库的中文乱码问题。     

路径差异敏感的包散射策略机制

数据中心网络中随机包散射设计策略有效地提升了链路的多路径利用率,但是容易造成乱序问题。介绍了由数据包乱序所引起的“伪丢失”现象,设计了基于分类路径的自适应包散射策略（Cparps）,根据路径的队列长度对路径进行分类,并将不同数据流隔离在不同类型的路径上进行随机散射。实验结果表明,在对称和非对称拓扑下Cparps协议的乱序次数和数据包个数都相对较少,说明Cparps可以有效缓解数据包的乱序。     

基于S3C2440A的液晶触摸乱序密码键盘设计

针对固定密码键盘存在的问题,设计了一种乱序密码键盘。给出了乱序密码键盘的组成,重点介绍了主要硬件和软件的设计方法。测试结果表明,乱序密码键盘的随机性很好,具有较高的安全性和保密性。     

MPLS故障恢复中的丢包与失序研究

该文分析了MPLS网络中的Makam和Haskin两种故障恢复机制,Makam机制会造成的数据包的丢失,Haskin可以避免这个问题,但会导致数据包失序的问题,该文提出了在Haskin的基础上使用缓冲区来避免数据包的失序,并给出了缓冲区大小的估计式,以及估计式中相关参数的近似计算方法.     

Emo-mirror: a proposal to support emotion recognition in children with autism spectrum disorders

Neural Computing and Applications - Autism spectrum disorder (ASD) is a neurodevelopmental disorder defined as persistent difficulty in maturing the socialization process. Health professionals have...