Li Gang

An artificial-intelligence approach to ECG analysis

The issue we address in this article is how to reduce the computational burden by using an algorithm based on a linear-approximation distance-thresholding compression technique combined with the backpropagation neural network method. We also address how to improve the training speed. The experimental results found with the MIT-BIH database show that the new algorithm is faster in convergence and more accurate in feature recognition than existing methods.

ECG analysis based on PCA and SOM

A new method for clustering analysis of QRS complexes is proposed. The method integrates principal component analysis (PCA) with self-organizing map neural network (SOM). The QRS complex feature is extracted based on PCA and the unsupervised SOM is employed to cluster the data. The characteristics and the behavior of the proposed method applying different SOM architectures are studied. The method is tested with the MIT-BIH database. It is demonstrated that QRS complexes feature can be presented by four largest principle components and the PCA results can be used to cluster analysis efficiently. The relationship between cluster results and clinical categories are also investigated in this paper.