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首页> 外文期刊>Latin America Transactions, IEEE (Revista IEEE America Latina) >ECG Feature Extraction for Automatic Classification of Ischemic Events
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ECG Feature Extraction for Automatic Classification of Ischemic Events

机译：心电图特征提取用于缺血事件的自动分类

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A fundamental part of the analysis of electrocardiographic signals (ECG) lies in the transformation of the data or samples of the signal, with the aim of improving the effectiveness of the processing and the subsequent interpretation of the results obtained from it. Thus, a certain number of features are extracted, which are expected to have high discriminatory properties. In this way, the next step of the analysis must be the construction of a vector of features, which must be a set of descriptors that completely transmit the essence of the signal being studied. These features can focus on different aspects of the signal, for example, durations of intervals or segments, amplitudes of different wave peaks, deviations with respect to the isoelectric line, and areas under curves, among others. In this paper we present a method that includes the detection of temporal events of the ECG signal, such as ST segment, T wave, QRS complex and QT interval, several features are extracted using these events. The features are validated with the K-means algorithm and the feature vector is used as input in the ischemia classifier.
机译：心电图信号（ECG）分析的基本部分在于信号数据或信号样本的转换，目的是提高处理效率以及对由此获得的结果进行后续解释。因此，提取了一定数量的特征，这些特征有望具有很高的区分性。这样，分析的下一步必须是特征向量的构建，特征向量必须是一组描述符，这些描述符完全传输正在研究的信号的本质。这些特征可以集中在信号的不同方面，例如间隔或分段的持续时间，不同波峰的幅度，相对于等电线的偏差以及曲线下的面积等。在本文中，我们提出了一种方法，包括检测ECG信号的时间事件，例如ST段，T波，QRS复数和QT间隔，使用这些事件提取一些特征。使用K均值算法验证特征，并将特征向量用作缺血分类器中的输入。

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