An ideal ANFIS-based approach for classification of biomedical signals employing wavelet sub-band energy-based characteristics feature

There is a critical linkage of the error detection and classification of various biomedical signals with the diagnosis of different abnormalities. In this paper, basing on the characteristic features of wavelet sub-band energy coefficient, an ideal approach has been implemented for ECG and EEG classification. The ECG and EEG signals are pre-processed using adaptive filter and further are decomposed into time-frequency representation by the use of wavelet transformation. These extracted wavelet coefficients are helpful in calculating certain statistical parameters. For the differentiation between normal and abnormal beats, the types of EEG and ECG beats are taken into consideration. For this classification, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. The real time signals are obtained from medical and diagnostic centers for the analysis purposes.