Sparse representation of ECG signals for automated recognition of cardiac arrhythmias

Raj Sandeep; Ray Kailash Chandra

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Sparse representation of ECG signals for automated recognition of cardiac arrhythmias

机译：心电图信号的稀疏表示可自动识别心律不齐

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As per the report of the World Health Organization (WHO), the mortalities due to cardiovascular diseases (CVDs) have increased to 50 million worldwide. Therefore, it is essential to have an efficient diagnosis of CVDs to enhance the healthcare in the clinical cardiovascular domain. The ECG signal analysis of a patient is a very popular tool to perform diagnosis of CVDs. However, due to the non-stationary nature of ECG signal and higher computational burden of the existing signal processing methods, the automated and efficient diagnosis remains a challenge.

机译：根据世界卫生组织（WHO）的报告，全世界因心血管疾病（CVD）引起的死亡人数已增加到5000万。因此，必须有效诊断CVD，以增强临床心血管领域的保健水平。患者的ECG信号分析是执行CVD诊断的非常流行的工具。但是，由于ECG信号的非平稳性和现有信号处理方法的较高计算负担，因此自动有效诊断仍然是一个挑战。

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来源
《Expert Systems with Application》 |2018年第9期|49-64|共16页
作者
Raj Sandeep; Ray Kailash Chandra;
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作者单位

Indian Inst Technol Patna, Dept Elect Engn, Bihta 801103, India;

Indian Inst Technol Patna, Dept Elect Engn, Bihta 801103, India;

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正文语种 eng
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关键词
Electrocardiogram signal; Sparse representation; Overcomplete dictionary; Least-square twin support vector machines; Artificial bee colony; Particle swarm optimization;

机译：心电图信号稀疏表示字典不完整最小二乘双支持向量机人工蜂群粒子群优化;

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专利

1. Robust automated cardiac arrhythmia detection in ECG beat signals [J] . de Albuquerque Victor Hugo C., Nunes Thiago M., Pereira Danillo R., Neural computing & applications . 2018,第3期

机译：ECG击败信号中强大的自动心脏心律失常检测
2. Automated recognition of cardiac arrhythmias using sparse decomposition over composite dictionary [J] . Raj Sandeep, Ray Kailash Chandra Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice . 2018,第期

机译：使用稀疏分解在复合词典中自动识别心脏心律失常
3. Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection [J] . Zhang Yuwei, Zhang Yuan, Lo Benny, Expert Systems . 2020,第1期

机译：可穿戴式ECG信号处理，可使用基于CFASE的特征选择自动进行心律失常分类
4. ECG Guided Automated Diagnostic Intervention of Cardiac Arrhythmias with Extra-Cardiac Noise Detection using Neural Network [C] . Binoy Sasmal, Sayan Roy International Conference on Optimization and Applications . 2021

机译：ECG使用神经网络引导心脏心律失常的自动诊断干预
5. Analysis of ECG signals and its application in prediction of sudden cardiac death. [D] . Gopalan, Varun. 2009

机译：心电图信号分析及其在心源性猝死预测中的应用。
6. Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal [O] . Udit Satija, Barathram Ramkumar, M. Sabarimalai Manikandan 2017

机译：基于噪声感知的字典学习的稀疏表示框架用于检测和消除ECG信号中的单个和组合噪声
7. Development of a Computer-Aided Application for Analyzing ECG Signals and Detection of Cardiac Arrhythmia Using Back Propagation Neural Network - Part I: Model Development [O] . Akinlolu A.Ponnle, Oludare Y. Ogundepo 2015

机译：开发用于分析ECG信号的计算机辅助应用和使用反向传播神经网络检测心律失常的检测 - 第I部分：模型开发

Sparse representation of ECG signals for automated recognition of cardiac arrhythmias

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