Gaussian ERP kernel classifier for pulse waveforms classification

While advances in sensor and signal processing techniques have provided effective tools for quantitative research on traditional Chinese pulse diagnosis (TCPD), the automatic classification of pulse waveforms is remained a difficult problem. To address this issue, this paper proposed a novel edit distance with real penalty (ERP)-based k-nearest neighbors (KNN) classifier by referring to recent progresses in time series matching and KNN classifier. Taking advantage of the metric property of ERP, we first develop a Gaussian ERP kernel, and then embed it into kernel difference-weighted KNN classifier. The proposed Gaussian ERP kernel classifier is evaluated on a dataset which includes 2470 pulse waveforms. Experimental results show that the proposed classifier is much more accurate than several other pulse waveform classification approaches.