An angina diagnostic system using fuzzy clustering and correlation

In this paper, we present a signal processing method capable of detecting angina in electrocardiograms. The adopted procedure is based on fuzzy clustering to reduce the amount of data sampling and a comparison with samples from a previously established database. By using the correlation method on the samples, it is possible to establish an initial indication of angina. The reduced number of samples of the clustering process turns the processing simpler and allows its hardware implementation. According to the tests conducted, the method achieves 85% correct diagnoses.