This study aims to present an efficient model for autodetection of cardiac arrhythmia by the diagnosis of self-affinity and identification of governing processes of a number of Electrocardiogram (ECG) signals taken from MIT-BIH database. In this work, the proposed model includes statistical methods to find the diagnosis pattern for detecting cardiac abnormalities which is useful for the computer aided system for arrhythmia detection. First, the Rescale Range (R/S) analysis has been employed for ECG signals to understand the scaling property of ECG signals. The value of Hurst exponent identifies the presence of abnormality in ECG signals taken for consideration with 92.58% accuracy. In this study, Higuchi method which deals with unifractality or monofractality of signals has been applied and it is found that unifractality is sufficient to detect arrhythmia with 91.61% accuracy. The Multifractal Detrended Fluctuation Analysis (MFDFA) has been used over the present signals to identify and confirm the multifractality. The nature of multifractality is different for arrhythmia patients and normal heart condition. The multifractal analysis is useful to detect abnormalities with 93.75% accuracy. Finally, the autocorrelation analysis has been used to identify the prevalent governing process in the present arrhythmic ECG signals and study confirms that all the signals are governed by stationary autoregressive methods of certain orders. In order to increase the overall efficiency, this present model deals with analyzing all the statistical features extracted from different statistical techniques for a large number of ECG signals of normal and abnormal heart condition. Finally, the result of present analysis altogether possibly indicates that the proposed model is efficient to detect cardiac arrhythmia with 99.3% accuracy.

中文翻译：

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使用统计特征和新的分类方法从 ECG 信号分析中诊断心律失常

本研究旨在通过诊断自亲和力和识别从 MIT-BIH 数据库中获取的许多心电图 (ECG) 信号的控制过程，提出一种有效的心律失常自动检测模型。在这项工作中，所提出的模型包括统计方法来找到检测心脏异常的诊断模式，这对于心律失常检测的计算机辅助系统很有用。首先，已对 ECG 信号采用重新缩放范围 (R/S) 分析，以了解 ECG 信号的缩放特性。Hurst 指数的值以 92.58% 的准确度识别 ECG 信号中是否存在异常。在这项研究中，已经应用了处理信号单分形或单分形的 Higuchi 方法，发现单分形足以以 91.61% 的准确率检测心律失常。多重分形去趋势波动分析 (MFDFA) 已用于对当前信号进行识别和确认多重分形。对于心律失常患者和正常心脏状况，多重分形的性质是不同的。多重分形分析有助于以 93.75% 的准确率检测异常。最后，自相关分析已被用于识别当前心律失常心电图信号中的普遍控制过程，研究证实所有信号均由特定阶数的平稳自回归方法控制。为了提高整体效率，本模型用于分析从不同统计技术中提取的大量正常和异常心脏状况的 ECG 信号的所有统计特征。最后，目前的分析结果可能表明，所提出的模型能够有效地检测心律失常，准确率达到 99.3%。