Noise components identification in biomedical signals based on Empirical Mode Decomposition

Hilbert-Huang transform (HHT) is composed of the empirical mode decomposition (EMD) as the first step of the procedure and Hilbert spectral analysis (HSA) as the second step. It is a recent tool in the analysis of signals originating from nonlinear processes as well as nonstationary signals. Empirical mode decomposition produces a set of intrinsic mode functions and the core idea is based on the assumption that any data consists of different simple intrinsic modes of oscillations. Statistical significance of the intrinsic mode functions and partial signal reconstruction are investigated in this paper. Application of Hilbert-Huang transform on biomedical signals such as ECG from MIT-BIH database and experimental respiratory signals acquired by means of accelerometers, reveal the adaptive nature of the method.