An RBF-LVQPNN model and its application to time-varying signal classification

A novel technique is proposed for maintaining the diversity of sample features and modeling imbalanced datasets in multi-channel time-varying signal classification. The RBF-LVQPNN consists of a time-varying signal input layer, an RBF process neuron hidden layer, an LVQ competition layer, a pattern layer, and a classifier. Dynamic clustering was used to divide samples in each pattern class into several pattern subclasses with similar features. Typical signal samples in the pattern subclasses are then used as the kernel centers for RBFPNs, to achieve embedding of the diversity signal class features. The output of the RBFPN layer was used as input to the LVQN. In the competition layer, the ‘winning’ neurons were selected to represent a pattern subclass. In the process of reorganizing pattern subclasses into pattern classes, subclass boundaries were combined into irregular class boundaries to reduce the overlap of decision spaces for different pattern classes. The RBF-LVQNN could improve the memory ability of typical signal features and discriminability of signals, realize structural and data constraints of the model, and improve the modeling property of imbalanced datasets. In this paper, the properties of the RBF-LVQPNN are analyzed and a comprehensive training algorithm is established. Experimental validation was performed with classification diagnoses from seven types of cardiovascular diseases based on 12-lead ECG signals. Results demonstrated that the proposed technique significantly improved both classification accuracy and generalizability comparing with other methods in the experiment.