Banerjee Swati, Mitra M
Department of Applied Physics, Faculty of Technology, University of Calcutta, 92, APC Road, Kolkata-700009, India.
J Med Eng Technol. 2013 May;37(4):264-72. doi: 10.3109/03091902.2013.794251. Epub 2013 May 23.
Myocardial infarction (MI) is a coronary artery disease acquired due to the lack of blood supply in one or more sections of the myocardium, resulting in necrosis in that region. It has different types based on the region of necrosis. In this paper, a statistical approach for classification of Anteroseptal MI (ASMI) is proposed. The first step of the method involves noise elimination and feature extraction from the Electrocardiogram (ECG) signals, using multi-resolution wavelet analysis and thresholding-based techniques. In the next step a classification scheme is developed using the nearest neighbour classification rule (NN rule). Both temporal and amplitude features relevant for automatic ASMI diagnosis are extracted from four chest leads v1-v4. The distance metric for NN classifier is calculated using both Euclidian distance and Mahalanobis distance. A relative comparison between these two techniques reveals that the later is superior to the former, as evident from the classification accuracy. The proposed method is tested and validated using the PTB diagnostic database. Classification accuracy for Mahalanobis distance and Euclidean distance-based NN rule are 95.14% and 81.83%, respectively.
心肌梗死（MI）是一种由于心肌一个或多个节段血液供应不足而导致的冠状动脉疾病，进而造成该区域坏死。根据坏死区域不同，其有不同类型。本文提出了一种用于分类前间隔心肌梗死（ASMI）的统计方法。该方法的第一步涉及使用多分辨率小波分析和基于阈值的技术，从心电图（ECG）信号中消除噪声并提取特征。下一步使用最近邻分类规则（NN规则）开发一种分类方案。从四个胸导联v1 - v4中提取与自动ASMI诊断相关的时间和幅度特征。NN分类器的距离度量使用欧几里得距离和马氏距离来计算。这两种技术的相对比较表明，从分类准确率来看，后者优于前者。所提出的方法使用PTB诊断数据库进行了测试和验证。基于马氏距离和欧几里得距离的NN规则的分类准确率分别为95.14%和81.83%。
