【作者】 李哲；

【导师】 刘明；

【作者基本信息】 河北大学 ， 通信与信息系统， 2015， 硕士

【摘要】 世界卫生组织公布的最新统计数据显示,世界范围内因为患慢性疾病而导致的死亡人数不断增加,经过更新的2010年至2011年全球10大致死原因,其中死于缺血性心脏病的人数以占2011年总数的12.9%共700万人而位居第一。临床医生大多根据心电图(electrocardiogram,ECG)波形来分析和诊断心脏病。ECG信号的自动分析对于当今纷杂的心电监护仪来说,无疑是最为关键的技术之一。ECG自动分析的任务主要包括对ECG信号预处理,特征提取等,最终目的是根据心电波形特点对其进行自动分类诊断。然而由于心电信号波形复杂多变和个体差异性对心电信号精确分类是非常困难的。现有心电信号分类算法取得了很多成就,但无论理论还是应用上都需进一步研究。稀疏表示的出现为心电信号处理提供了新切入点,被成功应用于人脸识别,图像去噪,心电信号压缩,检测等。本文设计了一种基于稀疏表示和最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的心电信号分类算法。论文主要研究成果包括:(1)我们在构造心电信号字典时对每一类聚类之后形成子字典,使得子字典内原子尽量相似,子字典间的原子尽量不同;(2)求解稀疏系数时并不是直接由各个字典组成的大字典求解而是对每一个子字典分别求解稀疏系数,不仅提高了分类准确率还节约了运算时间;(3)LS-SVM融合多特征稀疏系数,既保证充分挖掘数据的特点又能提高泛化能力。更多还原

【Abstract】 Cardiovascular disease is one of the main diseases which harm human health. The latest statistics promulgated by World Health Organization showed that chronic diseases caused increasing numbers of deaths worldwide. Moreover, the ischemic heart disease which caused700 million(12.9%)deaths occupied the first place among the 10 leading causes of death in2012. Most clinicians analysis and diagnosis of disease through the ECG waveforms, and automatic analysis of ECG signals is undoubtedly one of the key technologies for the ECG monitor today. The task of ECG automatic analysis mainly includes the ECG signal preprocessing, feature extraction, classification and so on.However, owing to the complexity and the diversity of ECG signals, it is very difficult to classify accurately. Existing ECG classification algorithm has made many achievements,but further studies are required both in theory and application. The emergence of sparse representation provides new point of penetration for ECG signal processing. Sparse representation has been successfully applied to face recognition, image denoising, ECG compression, ECG detection, etc. A new method that combines sparse representation with Least Squares Support Vector Machine-based classification is proposed for the classification of five types of ECG. Research of the dissertation is concentrated on the following aspects:(1)ECG sub-dictionaries were constructed by FCM, so that the atoms as similar as possible intra-class and as different as possible between inter-class;(2)Sparse coefficients were solved by each sub-dictionary instead of by a large dictionary consists of all sub-dictionaries, such manner not only improves the classification accuracy but also increase efficiency;(3)Multi-feature fused sparse coefficients by LS-SVM, both ensure exploit the characteristics of the data fully and improve the generalization ability.更多还原