Faculty of Mechanical Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland.
Sensors (Basel). 2022 Jun 30;22(13):4960. doi: 10.3390/s22134960.
Health monitoring and related technologies are a rapidly growing area of research. To date, the electrocardiogram (ECG) remains a popular measurement tool in the evaluation and diagnosis of heart disease. The number of solutions involving ECG signal monitoring systems is growing exponentially in the literature. In this article, underestimated Orthogonal Matching Pursuit (OMP) algorithms are used, demonstrating the significant effect of concise representation parameters on improving the performance of the classification process. Cardiovascular disease classification models based on classical Machine Learning classifiers were defined and investigated. The study was undertaken on the recently published PTB-XL database, whose ECG signals were previously subjected to detailed analysis. The classification was realized for class 2, class 5, and class 15 cardiac diseases. A new method of detecting R-waves and, based on them, determining the location of QRS complexes was presented. Novel aggregation methods of ECG signal fragments containing QRS segments, necessary for tests for classical classifiers, were developed. As a result, it was proved that ECG signal subjected to algorithms of R wave detection, QRS complexes extraction, and resampling performs very well in classification using Decision Trees. The reason can be found in structuring the signal due to the actions mentioned above. The implementation of classification issues achieved the highest Accuracy of 90.4% in recognition of 2 classes, as compared to less than 78% for 5 classes and 71% for 15 classes.
健康监测和相关技术是一个快速发展的研究领域。迄今为止，心电图（ECG）仍然是心脏病评估和诊断的常用测量工具。文献中涉及 ECG 信号监测系统的解决方案数量呈指数级增长。在本文中，使用了被低估的正交匹配追踪（OMP）算法，证明了简洁表示参数对提高分类过程性能的显著影响。基于经典机器学习分类器的心血管疾病分类模型被定义和研究。该研究是在最近发布的 PTB-XL 数据库上进行的，其 ECG 信号之前已经过详细分析。对 2 类、5 类和 15 类心脏病进行了分类。提出了一种新的检测 R 波的方法，并基于此方法确定了 QRS 波群的位置。开发了用于经典分类器测试的包含 QRS 段的 ECG 信号片段的新聚合方法。结果证明，经过 R 波检测算法、QRS 波群提取和重采样处理后的 ECG 信号在使用决策树进行分类时表现非常出色。原因可以在由于上述操作导致的信号结构中找到。分类问题的实现实现了对 2 类的识别的最高准确率为 90.4%，而对 5 类的识别准确率不到 78%，对 15 类的识别准确率为 71%。
