In this article, we present a resource-efficient approach for
electrocardiogram (ECG) based heartbeat classification using multi-feature
fusion and bidirectional long short-term memory (Bi-LSTM). The dataset
comprises five original classes from the MIT-BIH Arrhythmia Database: Normal
(N), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB),
Premature Ventricular Contraction (PVC), and Paced Beat (PB). Preprocessing
methods including the discrete wavelet transform and dual moving average
windows are used to reduce noise and artifacts in the raw ECG signal, and
extract the main points (PQRST) of the ECG waveform. Multi-feature fusion is
achieved by utilizing time intervals and the proposed under-the-curve areas,
which are inherently robust against noise, as input features. Simulations
demonstrated that incorporating under-the-curve area features improved the
classification accuracy for the challenging RBBB and LBBB classes from 31.4\%
to 84.3\% for RBBB, and from 69.6\% to 87.0\% for LBBB. Using a Bi-LSTM
network, rather than a conventional LSTM network, resulted in higher accuracy
(33.8\% vs 21.8\%) with a 28\% reduction in required network parameters for the
RBBB class. Multiple neural network models with varying parameter sizes,
including tiny (84k), small (150k), medium (478k), and large (1.25M) models,
are developed to achieve high accuracy \textit{across all classes}, a more
crucial and challenging goal than overall classification accuracy.

利用多特征融合和双向长短期记忆（Bi-LSTM）的资源高效方法对心电图（ECG）进行心跳分类，通过采用时间间隔和曲线下面积作为特征，改善 RBBB 和 LBBB 类别的分类准确率。

利用多特征融合和双向 LSTM 实现资源高效心跳分类

Resource-Efficient Heartbeat Classification Using Multi-Feature Fusion  and Bidirectional LSTM

Curvilinear structures, which include line-like continuous objects, are
fundamental geometrical elements in image-based applications. Reconstructing
these structures from images constitutes a pivotal research area in computer
vision. However, the complex topology and ambiguous image evidence render this
process a challenging task. In this paper, we introduce DeepBranchTracer, a
novel method that learns both external image features and internal geometric
characteristics to reconstruct curvilinear structures. Firstly, we formulate
the curvilinear structures extraction as a geometric attribute estimation
problem. Then, a curvilinear structure feature learning network is designed to
extract essential branch attributes, including the image features of centerline
and boundary, and the geometric features of direction and radius. Finally,
utilizing a multi-feature fusion tracing strategy, our model iteratively traces
the entire branch by integrating the extracted image and geometric features. We
extensively evaluated our model on both 2D and 3D datasets, demonstrating its
superior performance over existing segmentation and reconstruction methods in
terms of accuracy and continuity.

通过提取内外部几何特征，DeepBranchTracer 方法实现了对含有复杂拓扑和模糊图像证据的曲线结构的重建，具有优越的准确性和连续性。

DeepBranchTracer: 使用多特征学习的曲线结构重建的通用方法

DeepBranchTracer: A Generally-Applicable Approach to Curvilinear  Structure Reconstruction Using Multi-Feature Learning

With the rapid development of Internet technology, people have more and more
access to a variety of web page resources. At the same time, the current rapid
development of deep learning technology is often inseparable from the huge
amount of Web data resources. On the other hand, NLP is also an important part
of data processing technology, such as web page data extraction. At present,
the extraction technology of web page text mainly uses a single heuristic
function or strategy, and most of them need to determine the threshold
manually. With the rapid growth of the number and types of web resources, there
are still problems to be solved when using a single strategy to extract the
text information of different pages. This paper proposes a web page text
extraction algorithm based on multi-feature fusion. According to the text
information characteristics of web resources, DOM nodes are used as the
extraction unit to design multiple statistical features, and high-order
features are designed according to heuristic strategies. This method
establishes a small neural network, takes multiple features of DOM nodes as
input, predicts whether the nodes contain text information, makes full use of
different statistical information and extraction strategies, and adapts to more
types of pages. Experimental results show that this method has a good ability
of web page text extraction and avoids the problem of manually determining the
threshold.

本研究提出了一种基于多特征融合的网页文本提取算法，建立了小型神经网络，采用多种统计信息和提取策略，适应了更多页面类型，避免了手动确定阈值的问题。