Lee Jaewon, Shin Miyoung
Bio-Intelligence & Data Mining Laboratory, School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.
School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.
Bioengineering (Basel). 2023 Feb 2;10(2):196. doi: 10.3390/bioengineering10020196.
A method for accurately analyzing electrocardiograms (ECGs), which are obtained from electrical signals generated by cardiac activity, is essential in heart disease diagnosis. However, rhythms are typically obtained with relatively few data samples and similar characteristics, making them difficult to classify. To solve these issues, we proposed a novel method that distinguishes a given ECG rhythm using a beat score map (BSM) image. Through the proposed method, the associations between beats and previously used features, such as the R-R interval, were considered. Rhythm classification was implemented by training a convolutional neural network model and using transfer learning with the created BSM image. As a result, the proposed method for ECG rhythms with small data samples showed significant results. It also showed good performance in differentiating atrial fibrillation (AFIB) and atrial flutter (AFL) rhythms, which are difficult to distinguish due to their similar characteristics. The performance for rhythms with a small number of samples of the proposed method is 20% better than an existing method. In addition, the performance based on the F-1 score for classifying AFIB and AFL of the proposed method is 30% better than the existing method. This study solved the previous limitations caused by small sample numbers and similar rhythms.
一种用于精确分析心电图（ECG）的方法至关重要，心电图是由心脏活动产生的电信号获取得到的，在心脏病诊断中不可或缺。然而，心律通常是通过相对较少的数据样本和相似的特征获取的，这使得它们难以分类。为了解决这些问题，我们提出了一种新颖的方法，即使用搏动评分图（BSM）图像来区分给定的心电图心律。通过所提出的方法，考虑了搏动与先前使用的特征（如R-R间期）之间的关联。心律分类是通过训练卷积神经网络模型并使用创建的BSM图像进行迁移学习来实现的。结果，所提出的针对小数据样本的心电图心律方法显示出显著的成果。它在区分心房颤动（AFIB）和心房扑动（AFL）心律方面也表现出良好的性能，这两种心律由于特征相似而难以区分。所提出方法对于少量样本心律的性能比现有方法高出20%。此外，所提出方法基于F-1分数对AFIB和AFL进行分类的性能比现有方法高出30%。本研究解决了先前因样本数量少和心律相似而导致的局限性。
