Smíšek Radovan, Maršánová Lucie, Němcová Andrea, Vítek Martin, Kozumplík Jiří, Nováková Marie
Department of Biomedical Engineering, The Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 3058/10, 61600, Brno, Czech Republic.
Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 753/5, 62500, Brno, Czech Republic.
Med Biol Eng Comput. 2017 Aug;55(8):1473-1482. doi: 10.1007/s11517-016-1607-5. Epub 2016 Dec 31.
Nowadays, cardiovascular diseases represent the most common cause of death in western countries. Among various examination techniques, electrocardiography (ECG) is still a highly valuable tool used for the diagnosis of many cardiovascular disorders. In order to diagnose a person based on ECG, cardiologists can use automatic diagnostic algorithms. Research in this area is still necessary. In order to compare various algorithms correctly, it is necessary to test them on standard annotated databases, such as the Common Standards for Quantitative Electrocardiography (CSE) database. According to Scopus, the CSE database is the second most cited standard database. There were two main objectives in this work. First, new diagnoses were added to the CSE database, which extended its original annotations. Second, new recommendations for diagnostic software quality estimation were established. The ECG recordings were diagnosed by five new cardiologists independently, and in total, 59 different diagnoses were found. Such a large number of diagnoses is unique, even in terms of standard databases. Based on the cardiologists' diagnoses, a four-round consensus (4R consensus) was established. Such a 4R consensus means a correct final diagnosis, which should ideally be the output of any tested classification software. The accuracy of the cardiologists' diagnoses compared with the 4R consensus was the basis for the establishment of accuracy recommendations. The accuracy was determined in terms of sensitivity = 79.20-86.81%, positive predictive value = 79.10-87.11%, and the Jaccard coefficient = 72.21-81.14%, respectively. Within these ranges, the accuracy of the software is comparable with the accuracy of cardiologists. The accuracy quantification of the correct classification is unique. Diagnostic software developers can objectively evaluate the success of their algorithm and promote its further development. The annotations and recommendations proposed in this work will allow for faster development and testing of classification software. As a result, this might facilitate cardiologists' work and lead to faster diagnoses and earlier treatment.
如今，心血管疾病是西方国家最常见的死因。在各种检查技术中，心电图（ECG）仍然是用于诊断许多心血管疾病的极具价值的工具。为了基于心电图诊断一个人，心脏病专家可以使用自动诊断算法。该领域的研究仍然很有必要。为了正确比较各种算法，有必要在标准注释数据库（如定量心电图通用标准（CSE）数据库）上对它们进行测试。根据Scopus的数据，CSE数据库是被引用次数第二多的标准数据库。这项工作有两个主要目标。首先，在CSE数据库中添加了新的诊断结果，扩展了其原始注释。其次，建立了诊断软件质量评估的新建议。心电图记录由五位新的心脏病专家独立诊断，总共发现了59种不同的诊断结果。即使在标准数据库方面，如此大量的诊断结果也是独一无二的。基于心脏病专家的诊断，建立了四轮共识（4R共识）。这种4R共识意味着正确的最终诊断，理想情况下应该是任何测试分类软件的输出。将心脏病专家诊断的准确性与4R共识进行比较，是建立准确性建议的基础。准确性分别通过灵敏度 = 79.20 - 86.81%、阳性预测值 = 79.10 - 87.11% 和杰卡德系数 = 72.21 - 81.14% 来确定。在这些范围内，软件的准确性与心脏病专家的准确性相当。正确分类的准确性量化是独一无二的。诊断软件开发人员可以客观地评估其算法的成功程度，并促进其进一步发展。这项工作中提出的注释和建议将有助于分类软件的更快开发和测试。因此，这可能会方便心脏病专家的工作，并导致更快的诊断和更早的治疗。
