Panagiotou Christos, Dima Sofia-Maria, Mazomenos Evangelos B, Rosengarten James, Maharatna Koushik, Gialelis John, Morgan John
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:7326-9. doi: 10.1109/EMBC.2013.6611250.
This paper addresses the possibility of detecting presence of scar tissue in the myocardium through the investigation of vectorcardiogram (VCG) characteristics. Scarred myocardium is the result of myocardial infarction (MI) due to ischemia and creates a substrate for the manifestation of fatal arrhythmias. Our efforts are focused on the development of a classification scheme for the early screening of patients for the presence of scar. More specifically, a supervised learning model based on the extracted VCG features is proposed and validated through comprehensive testing analysis. The achieved accuracy of 82.36% (sensitivity 84.31%, specificity 77.36%) indicates the potential of the proposed screening mechanism for detecting the presence/absence of scar tissue.
本文通过研究向量心电图（VCG）特征来探讨检测心肌中瘢痕组织存在的可能性。瘢痕心肌是缺血导致心肌梗死（MI）的结果，并为致命性心律失常的表现创造了基础。我们的工作重点是开发一种用于早期筛查患者是否存在瘢痕的分类方案。更具体地说，提出了一种基于提取的VCG特征的监督学习模型，并通过全面的测试分析进行了验证。所达到的82.36%的准确率（敏感性84.31%，特异性77.36%）表明了所提出的筛查机制在检测瘢痕组织存在与否方面的潜力。
