A novel technique for the detection of myocardial dysfunction using ECG signals based on hybrid signal processing and neural networks

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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.

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Soft Computing