Heart disease prevention is one of the most important tasks for healthcare problems since more than 50 million people around the world are at the risk of cardiovascular disease. Traditionally, electrocardiography (ECG) signals play an important role in the diagnosis of cardiac disorder and arrhythmia detection since they reflect all the electrical activities of the heart. In the present study, we propose a novel technique for automatic detection of cardiac arrhythmia with one-lead ECG signals based upon tunable Q-factor wavelet transform (TQWT), variational mode decomposition (VMD), phase space reconstruction (PSR), and neural networks. First, ECG signals are decomposed into a set of frequency sub-bands with a number of decomposition levels by using the TQWT method without any preprocessing of QRS detection. Second, VMD is employed to decompose the sub-band of 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. They are selected to construct the reference variable for analysis. Third, phase space of the reference variable is reconstructed, in which the properties associated with the nonlinear ECG system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance has been utilized to derive features, which demonstrates 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 arrhythmia ECG signals. Finally, experiments are carried out on the MIT-BIH arrhythmia database to verify the effectiveness of the proposed method, in which 436 ECG signal fragments for one lead (MLII) from 28 persons of five classes of heart beats were extracted. By using the tenfold cross-validation style, the achieved average classification accuracy is reported to be 98.72%. Compared with various state-of-the-art methods, the proposed method demonstrates superior performance and has the potential to serve as a candidate for the automatic detection of myocardial dysfunction in the clinical application.

预防心脏病是解决医疗保健问题的最重要任务之一，因为全球有超过5000万人处于心血管疾病的风险中。传统上，心电图（ECG）信号在心脏疾病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。在本研究中，我们提出了一种基于可调Q因子小波变换（TQWT），变分模式分解（VMD），相空间重构（PSR）和神经网络的单导联心电图信号自动检测心律失常的新技术网络。首先，使用TQWT方法将ECG信号分解为具有多个分解级别的一组频率子带，而无需对QRS检测进行任何预处理。第二，VMD用于将ECG信号的子带分解为不同的固有模式，其中前四个固有模式包含了大部分ECG信号的能量，被认为是主要的固有模式。选择它们以构建用于分析的参考变量。第三，重构参考变量的相空间，其中保留与非线性ECG系统动力学相关的属性。三维（3D）PSR与欧几里得距离已被用来推导特征，这证明了正常与异常个体心跳之间ECG系统动力学的显着差异。第四，然后使用神经网络对正常（健康）和心律不齐的ECG信号之间的ECG系统动力学进行建模，识别和分类。最后，在MIT-BIH心律失常数据库上进行了实验，以验证该方法的有效性，该方法从5种心跳的28人中提取了一条铅（MLII）的436个ECG信号片段。通过使用十倍交叉验证样式，实现的平均分类准确度据报告为98.72％。与各种最新技术相比，该方法具有优越的性能，并有可能在临床应用中作为自动检测心肌功能障碍的候选方法。据报道，所获得的平均分类精度为98.72％。与各种最新技术相比，该方法具有优越的性能，并有可能在临床应用中作为自动检测心肌功能障碍的候选方法。据报道，所获得的平均分类精度为98.72％。与各种最新技术相比，该方法具有优越的性能，并有可能在临床应用中作为自动检测心肌功能障碍的候选方法。