Application of Artificial Neural Network and Empirical Mode Decomposition with Chaos Theory to Electrocardiography Diagnosis

We combined an artificial neural network (ANN) with empirical mode decomposition (EMD) and chaos theory for electrocardiography (ECG) signal recognition. The measuring circuit of the sensor and the LabVIEW human-machine interface developed in this study were used to measure and capture ECG signals. The stored ECG data were subjected to EMD into high and low frequencies. A chaotic error scatter map was generated by using master and slave chaotic systems, so as to obtain the chaotic eye coordinates of a specific ECG signal. A back-propagation neural network (BPNN) was applied for recognition. Fifty research subjects were enrolled for this study. The first half of the data was measured by a signal acquisition circuit, and the second half was provided by the Massachusetts Institute of Technology-Beth Israel Deaconess Medical Center (MIT-BIH). According to the analysis results, the proposed method has excellent accuracy in the classification of ECG signal recognition, with a recognition rate as high as 97%. Therefore, the ECG sensing system for automatic diagnosis designed in this study can effectively classify arrhythmia conditions and reduce manual identification costs and errors.