A Classification Method By The Nonlinear Dynamics Feature For Ecg Signals

With the rapid development of basic science in recent decades, the fruits of mathematicization of medical science have been enriched and the dependence of medical diagnosis has been reduced. And then, the diagnosis accuracy has been well improved. Obviously, some mathematics methods enhance this result. Especially, the nonlinear methods bring various great outcomes which are very important in medical research. In this paper, we mainly do some research work about the classification of ECG signals based on the theories of nonlinear dynamical system, and proposed a new method for ECG signal analysis. Finally, we present an intelligent system for arrhythmia diagnosis. This paper mainly includes the following aspects:Firstly, we found that the linear ECG signal classification method based on the characteristics of the waveform shape is hard to identify different types of ECG signal which have similar waveform, and the classification results not effective, more over, it is vulnerable to noise interference.Secondly, we propose a new nonlinear method for ECG signal classification which is able to identify four types ECG signal that are normal sinus rhythm, atrial fibrillation, ventricular fibrillation and ventricular tachycardia. And the center of it is to use the poincare sections to cut the trajectories in the phase space to gain the information hiding in its geometric structure.Once again, we use the ECG data from MIT database on experiment and obtained well accuracy of classification. This result verified the effectiveness of this method.Finally, based on the proposed method, we present an intelligent diagnosis system for the four types ECG signal. The experiment results obtained confirm that this intelligent system can enhance the ability to identify ECG signal efficiently...