Optimized Electrode Locations for Wearable Single-Lead ECG Monitoring Devices: A Case Study Using WFEES Modules Based on the LANS Method

Body surface potential mapping (BSPM) is a valuable tool for research regarding electrocardiograms (ECG). However, the BSPM system is limited by its large number of electrodes and wires, long installation time, and high computational complexity. In this paper, we designed a wearable four-electrode electrocardiogram-sensor (WFEES) module that measures six-channel ECGs simultaneously for ECG investigation. To reduce the testing lead number and the measurement complexity, we further proposed a method, the layered (A, N) square-based (LANS) method, to optimize the ECG acquisition and analysis process using WFEES modules for different applications. Moreover, we presented a case study of electrode location optimization for wearable single-lead ECG monitoring devices using WFEES modules with the LANS method. In this study, 102 sets of single-lead ECG data from 19 healthy subjects were analyzed. The signal-to-noise ratio of ECG, as well as the mean and coefficient of variation of QRS amplitude, was derived among different channels to determine the optimal electrode locations. The results showed that a single-lead electrode pair should be placed on the left chest above the electrode location of standard precordial leads V1 to V4. Additionally, the best orientation was the principal diagonal as the direction of the heart’s electrical axis

A lightweight piecewise linear synthesis method for standard 12-lead ECG signals based on adaptive region segmentation.

This paper presents a lightweight synthesis algorithm, named adaptive region segmentation based piecewise linear (ARSPL) algorithm, for reconstructing standard 12-lead electrocardiogram (ECG) signals from a 3-lead subset (I, II and V2). Such a lightweight algorithm is particularly suitable for healthcare mobile devices with limited resources for computing, communication and data storage. After detection of R-peaks, the ECGs are segmented by cardiac cycles. Each cycle is further divided into four regions according to different cardiac electrical activity stages. A personalized linear regression algorithm is then applied to these regions respectively for improved ECG synthesis. The proposed ARSPL method has been tested on 39 subjects randomly selected from the PTB diagnostic ECG database and achieved accurate synthesis of remaining leads with an average correlation coefficient of 0.947, an average root-mean-square error of 55.4μV, and an average runtime performance of 114ms. Overall, these results are significantly better than those of the common linear regression method, the back propagation (BP) neural network and the BP optimized using the genetic algorithm. We have also used the reconstructed ECG signals to evaluate the denivelation of ST segment, which is a potential symptom of intrinsic myocardial disease. After ARSPL, only 10.71% of the synthesized ECG cycles are with a ST-level synthesis error larger than 0.1mV, which is also better than those of the three above-mentioned methods

HiTIM: Hierarchical Task Information Mining for Few-Shot Action Recognition

Although the existing few-shot action recognition methods have achieved impressive results, they suffer from two major shortcomings. (a) During feature extraction, few-shot tasks are not distinguished and task-irrelevant features are obtained, resulting in the loss of task-specific critical discriminative information. (b) During feature matching, information critical to the features within the task, i.e., self-information and mutual information, is ignored, resulting in the accuracy being affected by redundant or irrelevant information. To overcome these two limitations, we propose a hierarchical task information mining (HiTIM) approach for few-shot action recognition that incorporates two key components: an inter-task learner (Kinter) and an attention-matching module with an intra-task learner (Kintra). The purpose of the Kinter is to learn the knowledge of different tasks and build a task-related feature space for obtaining task-specific features. The proposed matching module with Kintra consists of two branches: the spatiotemporal self-attention matching (STM) and correlated cross-attention matching (CM), which reinforce key spatiotemporal information in features and mine regions with strong correlations between features, respectively. The shared Kintra can further optimize STM and CM. In our method, we can use either a 2D convolutional neural network (CNN) or 3D CNN as embedding. In comparable experiments using two different embeddings in the five-way one-shot and five-way five-shot task, the proposed method achieved recognition accuracy that outperformed other state-of-the-art (SOTA) few-shot action recognition methods on the HMDB51 dataset and was comparable to SOTA few-shot action recognition methods on the UCF101 and Kinetics datasets

A Spectral–Temporal Patch-Based Missing Area Reconstruction for Time-Series Images

Clouds, cloud shadows (CCS), and numerous other factors will cause a missing data problem in passive remote sensing images. A well-known reconstruction method is the selection of a similar pixel (with an additional clear reference image) from the remaining clear part of an image to replace the missing pixel. Due to the merit of filling the missing value using a pixel acquired on the same image with the same sensor and the same date, this method is suitable for time-series applications when a time-series profile-based similar measure is utilized for selecting the similar pixel. Since the similar pixel is independently selected, the improper reference pixel or various accuracies obtained by different land covers causes the problem of salt-and-pepper noise in the reconstructed part of an image. To overcome these problems, this paper presents a spectral–temporal patch (STP)-based missing area reconstruction method for time-series images. First, the STP, the pixels of which have similar spectral and temporal evolution characteristics, is extracted using multi-temporal image segmentation. However, some STP have Missing Observations (STPMO) in the time series, which should be reconstructed. Next, for an STPMO, the most similar STP is selected as the reference STP; then, the mean and standard deviation of the STPMO is predicted using a linear regression method with the reference STP. Finally, the textural information, which is denoted by the spatial configuration of color or intensities of neighboring pixels, is extracted from the clear temporal-adjacent STP and “injected” into the missing area to obtain synthetic cloud-free images. We performed an STP-based missing area reconstruction experiment in Jiangzhou, Chongzuo, Guangxi with time-series images acquired by wide field view (WFV) onboard Chinese Gao Fen 1 on 12 different dates. The results indicate that the proposed method can effectively recover the missing information without salt-and-pepper noise in the reconstructed area; also, the reconstructed part of the image is consistent with the clear part without a false edge. The results confirm that the spectral information from the remaining clear part of the same image and textural information from the temporal-adjacent image can create seamless time-series images

Spatial-angular Quality-aware Representation Learning for Blind Light Field Image Quality Assessment

International audienc

Additional file 1 of Insights into myopic choroidal neovascularization based on quantitative proteomics analysis of the aqueous humor

Supplementary Material