Design and application of electrocardiograph diagnosis system based on multifractal theory

At present there are some ECG automatic diagnosis and identification system, which generally have a common characteristic that their research direction is more inclined to time domain analysis and frequency domain analysis. A large number of researchers have proved that ECG signal has multiple fractal characteristics, while using multi-fractal to analyze the chaotic system is also a trend. In this paper, the main research content is ECG automatic identification: ① Design and implementation of a differential threshold method for ECG signal automatic segmentation algorithm, the algorithm can automatically identify a segment of ECG in the ECG cycle, and ignore those ECG cycles, which are not complete ECG signal. ② Propose an algorithm to describe the data classification by using the multifractal theory to describe the data characteristics. The multi-fractal and semi-spectral characteristics of ECG and generalized Hurst exponent are used to train and test the neural network model. The accuracy of classification is 97%. ③ A complete ECG signal annotation system was built, which can automatically identify a segment of ECG sequence with multiple cycles and annotate each cycle. At the same time it can automatically ignored end-to-end incomplete ECG signal of the ECG sequence, that’s to say, this system has a better fault tolerance.