Survey of single‐target visual tracking methods based on online learning

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

A novel electrocardiogram arrhythmia classification method based on stacked sparse auto-encoders and softmax regression

Arrhythmia classification is crucial in electrocardiogram (ECG) based automatic cardiovascular disease diagnosis, e.g., to help prevent stroke or sudden cardiac death. However, the complex individual differences in ECG morphology make it challenging in accurately categorizing arrhythmia heartbeats. To promote robustness of the algorithm for individual differences, we propose a novel ECG arrhythmia classification method with stacked sparse auto-encoders (SSAEs) and a softmax regression (SF) model. The SSAEs is employed to hierarchically extract high-level features from huge amount of ECG data. Features are extracted automatically such that no individual difference in feature selection will bias extraction accuracy. Moreover, the input can be reconstructed completely by the features in each level of the auto-encoder. The SF is then trained to serve as a classifier for discriminating six different types of arrhythmia heartbeats. Computational experiments and comparative analyses are presented to validate the effectiveness of the theoretical models

Multi-lead model-based ECG signal denoising by guided filter

The electrocardiogram (ECG) denoising is of paramount importance for accurate disease diagnosis, but individual differences bring great difficulties for ECG denoising, especially for Dynamic Electrocardiography (DCG). In this paper, a multi-lead model-based ECG signal denoising method is proposed, in which a guided filter is inherently adapted to denoise ECG signal. For each person, a patient-specific statistical model will be constructed by sparse autoencoder (SAE) which can effectively preserve the detailed signal features. Thus, the guided signal producing by the statistical model can perform well in the guided filter. Especially, even the sudden morphological changes, the denoised ECG signals can still be conserved. The results on the 12-lead Arrhythmia Database and the MIT-BIH Arrhythmia Database demonstrate that the signal-to-noise ratio (SNR) improvement of the proposed method can reach as high as 21.54 dB, and the mean squared error (MSE) is less than 0.0401. Besides achievement of minimum signal distortion in comparisons with the major of the current denoising algorithms for complex noise environment, the proposed method demonstrate robustness in the complex interferences, especially in tracing the sudden morphological changes of ECG signals. Due to the remarkable superiority in preserving diagnostic and detail features of ECG signals, the proposed method can handle ECG signals with abnormal heart beats, and then can improve the accuracy detection of the disease.This research is partially supported by the National Natural Science Foundation of China (61673158, 61703133, 61473112), and the Natural Science Foundation of Hebei Province, China (F2016201186, F2017201222, F2018201070)

Automated detection and localization of myocardial infarction with staked sparse autoencoder and TreeBagger

Novel techniques in deep learning networks are proposed for the staked sparse autoencoder (SAE) and the bagged decision tree (TreeBagger), achieving significant improvement in detection and localization of myocardial infarction (MI) from single-lead electrocardiograph (ECG) signals. With our layer-wise training strategies, the SAE-based diagnostic feature extraction network can automatically and steadily extract the deep distinguishing diagnostic features of the single-lead ECG signals and avoid the vanishing gradient problem. This feature extraction network is formed by stacking shallow SAEs. In addition, to automatically learn the stable distinctive feature expression of the label-less input ECG signals, this feature extraction network adopts unsupervised learning. Moreover, TreeBagger classifier can optimize the results of multiple decision trees to more accurately detect and localize MI. The experiment and verification datasets include healthy controls, various types of MI with anterior, anterior lateral, anterior septal, anterior septal lateral, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior, and posterior lateral, from PTB diagnostic ECG database. The evaluation results show that the new techniques can effectively and accurately detect and localize the MI pathologies. For MI detection, the accuracy, the sensitivity, and the specificity rates achieve as high as 99.90%, 99.98%, and 99.52%, respectively. For MI localization, we obtain consistent results with the accuracy of 98.88%, sensitivity 99.95%, and specificity 99.87%. The comparative studies are conducted with the state-of-the-art techniques, and significant improvements by our methods are presented in the context. Success in the development of the accurate and comprehensive tool greatly helps the cardiologists in detection and localization of the single-lead ECG signals of MI.Published versio

Integrated multi-omics analyses reveal effects of empagliflozin on intestinal homeostasis in high-fat-diet mice

Summary: Obesity has become a global epidemic, associated with several chronic complications. The intestinal microbiome is a critical regulator of metabolic homeostasis and obesity. Empagliflozin, a sodium-glucose cotransporter 2 (SGLT2) inhibitor, has putative anti-obesity effects. In this study, we used multi-omics analysis to determine whether empagliflozin regulates metabolism in an obese host through the intestinal microbiota. Compared with obese mice, the empagliflozin-treated mice had a higher species diversity of gut microbiota, characterized by a reduction in the Firmicutes/Bacteroides ratio. Metabolomic analysis unambiguously identified 1,065 small molecules with empagliflozin affecting metabolites mainly enriched in amino acid metabolism, such as tryptophan metabolism. RNA sequencing results showed that immunoglobulin A and peroxisome proliferator-activated receptor signaling pathways in the intestinal immune network were activated after empagliflozin treatment. This integrative analysis highlighted that empagliflozin maintains intestinal homeostasis by modulating gut microbiota diversity and tryptophan metabolism. This will inform the development of therapies for obesity based on host-microbe interactions