Selective regional correlation for pattern recognition

In this paper, a novel correlation-based pattern classifier that relies on the analysis of time-frequency decomposition of a template and signals is proposed. Significant improvements in resolution and accuracy are obtained using this new classifier when compared to a conventional correlation-based one. The short-time Fourier transform, continuous wavelet transform, and S-transform are considered in the time-frequency decomposition process. To evaluate the performance of the proposed scheme, numerical studies are performed on a set of synthetic test signals, and excellent results have been obtained. This paper also presents an illustrative example where two types of heart sounds are classified. The classification error percentage for the heart sounds using the new classifier is only 6.670% as compared to 56.67% when a general correlation-based classifier is used.