Application of Empirical Mode Decomposition and Extension Neural Network Type-3 to Feature Diagnosis of Electrocardiograms

摘要

We propose to combine extension neural network type-3 (ENN-3) with the chaos theory and empirical mode decomposition (EMD) for electrocardiography (ECG) identification. ECG signals are measured and captured by the developed hardware measuring circuit and LabVIEW human-machine interface, and the stored ECG data are subjected to EMD at high- and low-frequency signals. A chaos dynamic error scatter map is formed using master and slave chaotic systems in order to obtain the chaos eye coordinates of a specific ECG signal, and ENN-3 is used for identification. There are 50 research participants in this study; the first half of the data are measured using a signal capturing circuit and a wrist patch-type ECG sensor (patch electrodes), while the second half are provided by Massachusetts Institute of Technology-Beth Israel Deaconess Medical Center (MIT-BIH). Analysis results show that the method proposed in this study has a higher accuracy in the classification of ECG signals, and that the recognition rate is as high as 100%. The recognition result was compared with those of ENN-3, the multilayer neural network, extension method, and ENN. The results showed that ENN-3 has a higher recognition accuracy rate than the other three algorithms, the difference being as much as 8%. Therefore, the autodiagnosis ECG system designed in this study can effectively classify arrhythmia and reduce the high cost of manual identification.