Department of Electronics & Communication Engineering, Rajiv Gandhi University, Itanagar, Arunachal Pradesh, India.
School of Electronics Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India.
Biomed Tech (Berl). 2021 May 4;66(5):489-501. doi: 10.1515/bmt-2020-0329. Print 2021 Oct 26.
Myocardial infarction (MI) happens when blood stops circulating to an explicit segment of the heart causing harm to the heart muscles. Vectorcardiography (VCG) is a technique of recording direction and magnitude of the signals that are produced by the heart in a 3-lead representation. In this work, we present a technique for detection of MI in the inferior portion of heart using short duration VCG signals. The raw signal was pre-processed using the median and Savitzky-Golay (SG) filter. The Stationary Wavelet Transform (SWT) was used for time-invariant decomposition of the signal followed by feature extraction. The selected features using minimum-redundancy-maximum-relevance (mRMR) based feature selection method were applied to the supervised classification methods. The efficacy of the proposed method was assessed under both class-oriented and a more real-life subject-oriented approach. An accuracy of 99.14 and 89.37% were achieved respectively. Results of the proposed technique are better than existing state-of-art methods and used VCG segment is shorter. Thus, a shorter segment and a high accuracy can be helpful in the automation of timely and reliable detection of MI. The satisfactory performance achieved in the subject-oriented approach shows reliability and applicability of the proposed technique.
心肌梗死（MI）发生在血液停止流向心脏的特定区域时，导致心肌受损。向量心电图（VCG）是一种记录心脏在三导联表示中产生的信号方向和幅度的技术。在这项工作中，我们提出了一种使用短持续时间 VCG 信号检测心脏下部 MI 的技术。原始信号使用中值和 Savitzky-Golay（SG）滤波器进行预处理。使用平稳小波变换（SWT）对信号进行时不变分解，然后进行特征提取。使用基于最小冗余最大相关性（mRMR）的特征选择方法选择特征，并将其应用于监督分类方法。在面向类和更现实的面向对象的方法下评估了所提出方法的功效。分别实现了 99.14%和 89.37%的准确率。与现有的最先进方法相比，所提出技术的结果更好，并且使用的 VCG 段更短。因此，较短的片段和较高的准确性有助于 MI 的自动及时和可靠检测。面向对象方法中取得的令人满意的性能表明了所提出技术的可靠性和适用性。
