Accurate detection of arrhythmia signal types is of great significance for the early detection of heart disease and its subsequent treatment. The primary purpose of this study is to explore an electrocardiogram (ECG) classification system to improve its performance and achieve excellent computing performance, especially for large sample datasets. We classified ECG signals using the Hurst exponent, which is an ECG feature extracted by multifractal detrended moving average cross-correlation analysis (MF-XDMA). In addition, we used multifractal methods such as multifractal detrended fluctuation analysis (MF-DFA), multifractal detrended cross-correlation analysis (MF-DCCA) and multifractal detrended moving average (MF-DMA) to extract the features of ECG signals, and we used a support vector machine (SVM) to classify the four types of feature data. The experimental results show that MF-XDMA-SVM has the best classification performance for atrial premature beat (APB) and bigeminy signals, which indicates that MF-XDMA-SVM is the most effective for the extraction of ECG signal sequence features among the four multifractal models.

中文翻译：

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使用多重分形去趋势移动平均互相关分析进行心电图分类

准确检测心律失常信号类型对于心脏病的早期发现及其后续治疗具有重要意义。本研究的主要目的是探索心电图（ECG）分类系统，以提高其性能并实现出色的计算性能，尤其是对于大样本数据集。我们使用赫斯特指数对 ECG 信号进行分类，这是通过多重分形去趋势移动平均互相关分析 (MF-XDMA) 提取的 ECG 特征。此外，我们使用多重分形去趋势波动分析（MF-DFA）、多重分形去趋势互相关分析（MF-DCCA）和多重分形去趋势移动平均（MF-DMA）等多重分形方法来提取心电信号的特征，我们使用支持向量机（SVM）对四种类型的特征数据进行分类。