The University of Mashreq, Research Center, Baghdad, Iraq.
Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad 10021, Iraq.
Comput Intell Neurosci. 2022 Aug 5;2022:1094830. doi: 10.1155/2022/1094830. eCollection 2022.
The electrocardiogram, also known as an electrocardiogram (ECG), is considered to be one of the most significant sources of data regarding the structure and function of the heart. In order to obtain an electrocardiogram, the contractions and relaxations of the heart are first captured in the proper recording medium. Due to the fact that irregularities in the functioning of the heart are reflected in the ECG indications, it is possible to use these indications to diagnose cardiac issues. Arrhythmia is the medical term for the abnormalities that might occur in the regular functioning of the heart (rhythm disorder). Environmental and genetic variables can both play a role in the development of arrhythmias. Arrhythmias are reflected on the ECG sign, which depicts the same region regardless of where in the heart they occur; thus, they may be seen in ECG signals. This is how arrhythmias can be detected. Due to the time limits of this study, the ECG signals of individuals who were healthy, as well as those who suffered from arrhythmias were divided into 10-minute segments. The arithmetic mean approach is one of the fundamental statistical factors. It is used to construct the feature vectors of each received wave and interval, and these vectors offer information regarding arrhythmias in accordance with the agreed-upon temporal restrictions. In order to identify the heart arrhythmias, the obtained feature vectors are fed into a classifier that is based on a multilayer perceptron neural network. In conclusion, ROC analysis and contrast matrix are utilised in order to evaluate the overall correct classification result produced by the ECG-based classifier. Because of this, it has been demonstrated that the method that was recommended has high classification accuracy when attempting to diagnose arrhythmia based on ECG indications. This research makes use of a variety of diagnostic terminologies, including ECG signal, multilayer perceptron neural network, signal processing, disease diagnosis, and arrhythmia diagnosis.
心电图，也被称为心电图（ECG），被认为是心脏结构和功能最重要的数据来源之一。为了获得心电图，首先需要将心脏的收缩和舒张记录在适当的记录介质中。由于心脏功能的不规则会反映在心电图指示中，因此可以使用这些指示来诊断心脏问题。心律失常是指心脏正常功能可能出现的异常（节律紊乱）的医学术语。环境和遗传因素都可能导致心律失常的发生。心律失常反映在心电图信号上，无论它们发生在心脏的哪个部位，心电图信号都能描绘出相同的区域；因此，它们可能出现在心电图信号中。这就是如何检测心律失常。由于本研究的时间限制，将健康人和心律失常患者的心电图信号分为 10 分钟的片段。算术平均值方法是基本统计因素之一。它用于构建每个接收波和间隔的特征向量，这些向量根据约定的时间限制提供有关心律失常的信息。为了识别心律失常，将获得的特征向量输入基于多层感知器神经网络的分类器。总之，利用 ROC 分析和对比矩阵来评估基于 ECG 的分类器产生的整体正确分类结果。因此，已经证明，在尝试根据心电图指示诊断心律失常时，所推荐的方法具有很高的分类准确性。本研究使用了多种诊断术语，包括心电图信号、多层感知器神经网络、信号处理、疾病诊断和心律失常诊断。
