Electrocardiography (ECG) signals play an important role in the cardiac disorder diagnosis and arrhythmia detection since they reflect all the electrical activities of the heart and include information about heart function and heart conditions. Due to the subtle alterations in the amplitude, duration and morphology of the ECG, the development of computer-based intelligent system in the arrhythmia diagnosis field is attractive in terms of the amount of data and the importance of the data it contains for the classification of heartbeats from different types of arrhythmias. In the present study we propose a novel technique for the automatic detection of cardiac arrhythmia with one-lead ECG signals based upon variational mode decomposition (VMD), Shannon energy envelope, phase space reconstruction (PSR) and deterministic learning theory. First, VMD is employed to decompose the ECG signals into different intrinsic modes, in which the first four intrinsic modes contain the majority of the ECG signals’ energy and are considered to be the predominant intrinsic modes. Second, Shannon energy is used to extract the characteristic envelope of predominant intrinsic modes. Third, phase space of the Shannon energy envelope (SEE) is reconstructed, in which properties associated with the nonlinear ECG characteristics are preserved. Three-dimensional (3D) phase space reconstruction (PSR) together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in ECG system dynamics between normal versus abnormal individual heartbeats. Fourth, neural networks are then used to model, identify and classify ECG system dynamics between normal (healthy) and arrhythmic ECG signals based on deterministic learning theory. Finally, experiments are carried out on the MIT-BIH arrhythmia database to verify the effectiveness of the proposed method, in which 626 ECG signal fragments for one lead (MLII) from 28 persons of five classes of heartbeats were extracted. These five classes are normal sinus rhythm (NSR), premature ventricular contraction (PVC), paced beat (PB), left bundle branch block (LBBB), and right bundle branch block (RBBB). By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 98.72\(\%\). Experimental results verify the effectiveness of the proposed method and indicate that it has the potential to serve as a candidate for the automatic detection of myocardial dysfunction in the clinical application.

心电图 (ECG) 信号在心脏疾病诊断和心律失常检测中起着重要作用，因为它们反映了心脏的所有电活动，包括有关心脏功能和心脏状况的信息。由于心电图的幅度、持续时间和形态的细微变化，在心律失常诊断领域开发基于计算机的智能系统在数据量及其包含的数据对心律失常分类的重要性方面具有吸引力。来自不同类型心律失常的心跳。在本研究中，我们提出了一种基于变分模式分解 (VMD)、香农能量包络、相空间重建 (PSR) 和确定性学习理论的单导联 ECG 信号自动检测心律失常的新技术。首先，VMD用于将ECG信号分解为不同的固有模式，其中前四种固有模式包含ECG信号的大部分能量并且被认为是主要的固有模式。其次，香农能量用于提取主要固有模式的特征包络。第三，重建香农能量包络 (SEE) 的相空间，其中保留了与非线性 ECG 特性相关的属性。三维 (3D) 相空间重建 (PSR) 和欧几里得距离 (ED) 已被用于推导特征，这表明正常与异常个体心跳之间的 ECG 系统动力学存在显着差异。第四，然后使用神经网络进行建模，基于确定性学习理论，识别和分类正常（健康）和心律失常心电图信号之间的心电图系统动态。最后，在MIT-BIH心律失常数据库上进行了实验，验证了所提出方法的有效性，从28人的5类心跳中提取了626个单导联（MLII）的心电信号片段。这五类是正常窦性心律 (NSR)、室性早搏 (PVC)、起搏 (PB)、左束支传导阻滞 (LBBB) 和右束支传导阻滞 (RBBB)。通过使用 10 折交叉验证样式，报告的平均分类准确率为 98.72 其中提取了 28 人的 5 类心跳的 626 个单导联 (MLII) 的心电信号片段。这五类是正常窦性心律 (NSR)、室性早搏 (PVC)、起搏 (PB)、左束支传导阻滞 (LBBB) 和右束支传导阻滞 (RBBB)。通过使用 10 折交叉验证样式，报告的平均分类准确率为 98.72 其中提取了 28 人的 5 类心跳的 626 个单导联 (MLII) 的 ECG 信号片段。这五类是正常窦性心律 (NSR)、室性早搏 (PVC)、起搏 (PB)、左束支传导阻滞 (LBBB) 和右束支传导阻滞 (RBBB)。通过使用 10 折交叉验证样式，报告的平均分类准确率为 98.72\(\%\)。实验结果验证了所提出方法的有效性，表明其具有作为临床应用中心肌功能障碍自动检测的候选者的潜力。