摘要翻译：
心电特征提取在大多数心脏疾病的诊断中起着重要的作用。心电信号中的一个心动周期由P-QRS-T波组成。该特征提取方案确定了心电信号中的振幅和间隔，以供后续分析。P-QRS-T段的波幅值和间期值决定着每个人的心脏功能。近年来，人们对心电信号进行了大量的研究和分析。所提出的方案主要基于模糊逻辑方法、人工神经网络(ANN)、遗传算法(GA)、支持向量机(SVM)等信号分析技术。所有这些技术和算法都有其优点和局限性。本文讨论了以前文献中提出的从心电信号中提取特征的各种技术和变换。此外，本文还对国内外学者提出的各种心电信号特征提取方法进行了比较研究。
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英文标题：
《ECG Feature Extraction Techniques - A Survey Approach》
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作者：
S. Karpagachelvi, M.Arthanari, M. Sivakumar
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Physics        物理学
二级分类：Medical Physics        医学物理学
分类描述：Radiation therapy. Radiation dosimetry. Biomedical imaging modelling.  Reconstruction, processing, and analysis. Biomedical system modelling and analysis. Health physics. New imaging or therapy modalities.
放射治疗。辐射剂量学。生物医学成像建模。重建、处理和分析。生物医学系统建模与分析。健康物理学。新的成像或治疗方式。
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英文摘要：
  ECG Feature Extraction plays a significant role in diagnosing most of the cardiac diseases. One cardiac cycle in an ECG signal consists of the P-QRS-T waves. This feature extraction scheme determines the amplitudes and intervals in the ECG signal for subsequent analysis. The amplitudes and intervals value of P-QRS-T segment determines the functioning of heart of every human. Recently, numerous research and techniques have been developed for analyzing the ECG signal. The proposed schemes were mostly based on Fuzzy Logic Methods, Artificial Neural Networks (ANN), Genetic Algorithm (GA), Support Vector Machines (SVM), and other Signal Analysis techniques. All these techniques and algorithms have their advantages and limitations. This proposed paper discusses various techniques and transformations proposed earlier in literature for extracting feature from an ECG signal. In addition this paper also provides a comparative study of various methods proposed by researchers in extracting the feature from ECG signal.
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PDF链接：
https://arxiv.org/pdf/1005.0957