Comparison of the non linear energy operator and the Hilbert transform in the estimation of the instantaneous amplitude and frequency

The instantaneous amplitude (ai) and frequency (fi) parameters of a biomedical signal can be useful for identification of signal physiological states or state changes. In this article the features ai and fi of simulated and experimental (real EEG and ECG) signals, are estimated using three methods: one based on the Hilbert Transform (HT), a modified version of this that improves the fi estimation in experimental signals (HTM), and the energy separation algorithm (DESA1), based on Teager’s energy operator (TEO). The algorithm comparison is made using the average relative error obtained in the signals’ demodulation process, their noise sensitivity, and computational efficiency. The obtained results showed that the HTM method produces the least fi estimation errors in noisy signals, and depending on the kind of signal considered, DESA1 and HTM methods produce the least ai estimation errors.