ECG beat classification using GreyART network
The grey relational grade is a similarity measure. On the basis of the grey relational grade, an adaptive resonant theory (ART) type network, GreyART, has been developed. When the GreyART is used to classify a dataset with varying amount of data, the measurement between two specific data in the dataset may vary since the measurement is affected by new added data. In this case, the grey relational grade is not a global measure. As the measurement varies, in the GreyART, it is hard to use a fixed vigilance threshold value for determining whether the current input data belong to one of the existing clusters or become the template of a new online-created cluster. A method to solve this problem has been proposed and then applied to develop an electrocardiogram (ECG) beat classifier. The proposed ECG beat classification involves two phases. One is the off-line learning phase. With the proposed performance index, the product of the classification accuracy and the partition quality, an optimal value for the vigilance threshold and the corresponding cluster centres from the learning results can be determined. The other is the online examining phase, which classifies the input ECG beats. In this phase, the vigilance threshold value and the initial cluster centres are the optimal ones obtained in the learning phase. Under these conditions, the GreyART network enables real-time classification of ECG beats. Simulation results show that the proposed network achieves a good accuracy with a good computational efficiency for ECG beat classification problems.
