Department of Electrical Engineering and Information Technologies, University "Federico II" of Naples, Italy.
J Healthc Eng. 2013;4(4):465-504. doi: 10.1260/2040-2295.4.4.465.
Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.
计算机系统用于心电图（ECG）分析，可辅助临床医生完成繁琐的任务（例如，在重症监护病房进行动态心电图监测）或快速检测危险事件（例如，心室颤动）。心电图与临床应用（心律失常检测和心率变异性分析）一起，目前正在生物识别领域（人类识别）中进行研究，这是一个新兴的领域，受到越来越多的关注。临床应用的方法在生物识别方面既有差异也有相似之处。本文从模式识别的角度回顾了心电图处理方法。特别是，我们专注于常用于心跳分类的特征。考虑到该领域文献浩繁，本综述篇幅有限，我们仅详细讨论了几种分类器（人工神经网络和支持向量机），因为它们较为流行；但是，也将提到其他技术，例如隐马尔可夫模型和卡尔曼滤波。
