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Myocardial infarction (MI) causes rapid and permanent damage to the heart muscle. Therefore, it can deteriorate the myocardial structure and function if not timely diagnosed and treated. However, it is difficult to determine the precise localization of MI based on vectorcardiogram (VCG) due to the existing studies ignore the spatiotemporal features of VCG.In this paper, a precise MI localization method was proposed based on Tucker decomposition. The multi-scale characteristics of wavelet transform and the spatiotemporal characteristics of VCG were used to construct the VCG tensor containing the local and the spatiotemporal information. The VCG tensor was compressed in the time dimension based on Tucker decomposition to remove redundant information and extract the local spatiotemporal features. The features were fed back to the TreeBagger classifier.The proposed method achieved a total accuracy of 99.80% for 11 types of MI on the benchmark Physikalisch-Technische Bundesanstalt database. The area under the receiver operating characteristic curves and precision-recall curves of each kind of ECG signal was more than 0.88.The proposed algorithm effectively realized the classification of normal ECG and 11 categories of MI using VCG.Therefore, this study provides new ideas for the intelligent diagnosis of MI based on VCG.

Early accurate detection of inferior myocardial infarction is an important way to reduce the mortality from inferior myocardial infarction. Regrading the existing problems in the detection of inferior myocardial infarction, complex model structures and redundant features, this paper proposed a novel inferior myocardial infarction detection algorithm. Firstly, based on the clinic pathological information, the peak and area features of QRS and ST-T wavebands as well as the slope feature of ST waveband were extracted from electrocardiogram (ECG) signals leads Ⅱ, Ⅲ and aVF. In addition, according to individual features and the dispersion between them, we applied genetic algorithm to make judgement and then input the feature with larger degree into support vector machine (SVM) to realize the accurate detection of inferior myocardial infarction. The proposed method in this paper was verified by Physikalisch-Technische Bundesanstalt (PTB) diagnostic electrocardio signal database and the accuracy rate was up to 98.33%. Conforming to the clinical diagnosis and the characteristics of specific changes in inferior myocardial infarction ECG signal, the proposed method can effectively make precise detection of inferior myocardial infarction by morphological features, and therefore is suitable to be applied in portable devices development for clinical promotion.下壁心肌梗死的早期检测是降低下壁心肌梗死死亡率的重要手段。针对现有的下壁心肌梗死检测存在模型结构复杂、特征冗余的问题，本文提出一种下壁心肌梗死检测算法。该方法与临床病理信息相结合，提取心电信号Ⅱ、Ⅲ和 aVF 三个导联中的 QRS 波段和 ST-T 波段的峰值、面积以及 ST 的斜率等特征，并由遗传算法依据单个特征以及特征之间的离散度进行判断，选择区分度较大的特征送入支持向量机（SVM），实现下壁心肌梗死检测。本文采用德国国家计量学研究所（PTB）诊断心电数据库对该方法进行验证，准确率为 98.33%。本文方法有效地利用心电信号形态学特征实现了下壁心肌梗死的检测，符合临床医生诊断以及下壁心肌梗死心电信号特异性改变的特点，适合临床广泛推广以及便携式设备应用的实现。.

The ageing of the population drives the rapid increase of cognitive disorders, which cause a heavy burden for families and nations. It is important to screen and interfere with cognitive disorders at the earlier stage, but we are short of affordable and effective approaches. In this talk, we will discuss our attempt in the design of the multi-mode detection and rehabilitation methods for cognitive disorders using computational intelligence algorithms, wearable devices and rehabilitation robots.

This paper presents a pattern recognition model using multiple features and the kernel extreme learning machine (ELM), improving the accuracy of automatic epilepsy diagnosis. After simple preprocessing, temporal- and wavelet-based features are extracted from epileptic EEG signals. A combined kernel-function-based ELM approach is then proposed for feature classification. To further reduce the computation, Cholesky decomposition is introduced during the process of calculating the output weights. The experimental results show that the proposed method can achieve satisfactory accuracy with less computation time.

Bonnet polishing has been successfully used for ultra-precision machining of the large-aperture aspheric optics. To accomplish online monitoring of executive condition of the removing function and state of the polishing machine, key technological parameters and processing condition factors need to be acquired in real time and coupled back. A novel data fusion method which combines hardware synchronous signal and correcting algorithm based on the principle of linear extrapolation is proposed in allusion to data acquisition distortion caused by asynchronous sampling frequency and communication delay between different systems. The estimate values are obtained by dynamically correcting original acquired data, and the upper bound of the error is deduced with the speed and the acceleration of the signal. The simulation results showed that the data fusion method can significantly reduce the error of data acquisition. The experimental results confirmed that the data fusion method can be effectively applied to monitor the optics bonnet polishing process online.

Visual tracking is a popular and challenging topic in computer vision and robotics. Owing to changes in the appearance of the target and complicated variations that may occur in various scenes, online learning scheme is necessary for advanced visual tracking framework to adopt. This paper briefly introduces the challenges and applications of visual tracking and focuses on discussing the state-of-the-art online-learning-based tracking methods by category. We provide detail descriptions of representative methods in each category, and examine their pros and cons. Moreover, several most representative algorithms are implemented to provide quantitative reference. At last, we outline several trends for future visual tracking research.

In neuro-rehabilitation after stroke, the conventional constrained induced movement therapy (CIMT) has been well-accepted. Existing bilateral trainings are mostly on mirrored symmetrical motion. However, complementary bilateral movements are dominantly involved in activities of daily living (ADLs), and functional bilateral therapies may bring better skill transfer from trainings to daily life. Neurophysiological evidence is also growing. In this work, we firstly introduce our bilateral arm training system realized with a haptic interface and a motion sensor, as well as the tasks that have been designed to train both the manipulation function of the paretic arm and coordination of bilateral upper limbs. Then, we propose quantitative measures for functional assessment of complementary bilateral training performance, including kinematic behavior indices, smoothness, submovement and bimanual coordination. After that, we describe the experiments with healthy subjects and the results with respect to these quantitative measures. Feasibility and sensitivity of the proposed indices were evaluated through comparison of unilateral and bilateral training outcomes. The proposed bilateral training system and tasks, as well as the quantitative measures, have been demonstrated effective for training and assessment of unilateral and bilateral arm functions.

It is widely believed that human brain is a complicated network and many neurological disorders such as Alzheimer's disease (AD) are related to abnormal changes of the brain network architecture. In this work, we present a kernel-based method to establish a network for each subject using mean cortical thickness, which we refer to hereafter as the individual's network. We construct individual networks for 83 subjects, including AD patients and normal controls (NC), which are taken from the Open Access Series of Imaging Studies database. The network edge features are used to make prediction of AD/NC through the sophisticated machine learning technology. As the number of edge features is much more than that of samples, feature selection is applied to avoid the adverse impact of high-dimensional data on the performance of classifier. We use a hybrid feature selection that combines filter and wrapper methods, and compare the performance of six different combinations of them. Finally, support vector machines are trained using the selected features. To obtain an unbiased evaluation of our method, we use a nested cross validation framework to choose the optimal hyper-parameters of classifier and evaluate the generalization of the method. We report the best accuracy of 90.4 % using the proposed method in the leave-one-out analysis, outperforming that using the raw cortical thickness data by more than 10 %.