Time series adapted supervised fuzzy discretization: an application to ECG signals

In this study, a new method called supervised fuzzy discretization (SFD), which can be used without having expertise on data, is proposed for classifying time series datasets. Because an ECG signal has a partially stationary characteristic, its classification process is more difficult than it would be for completely stationary signals. On the other hand, because the method proposed can be used without having expertise on the data, comprehensive data like ECG signals are enough to introduce one such method. To prove the efficacy of the SFD, RR intervals selected from a common ECG database are used in the classification experiments. Some parameters, such as the coefficients of discretization, equal time slicing, learning rate, and momentum, are analyzed for the highest level of success in classification. A new mechanism called an inconsistency detector is suggested for increasing the level of success in supervised learning by adjusting the learning rate. The best results of the SFD method are compared with those of other studies in the same database, which hopefully establishes the proposed method as worth investigating in other areas because of its projected success.