On modified EMD: Selective extrema analysis

The Empirical Mode Decomposition (EMD) algorithm was introduced as the first step of the Hilbert-Huang Transform, proposed by Huang et al. (1998). EMD decomposes a signal into so-called Intrinsic Mode Functions (IMFs) in a systematic way. Since then, various versions of EMD have been developed, addressing weaknesses of the original EMD procedure and aiming to optimize the original algorithm in a number of ways. This paper The Empirical Mode Decomposition (EMD) algorithm was introduced as the first step of the Hilbert-Huang Transform, proposed by Huang et al. (1998). EMD decomposes a signal into so-called Intrinsic Mode Functions (IMFs) in a systematic way. Since then, various versions of EMD have been developed, addressing weaknesses of the original EMD procedure and aiming to optimize the original algorithm in a number of ways. This paper proposes to use selective extrema analysis while generating IMFs with two goals. One is to reduce/control the number of IMFs a signal is decomposed into with a small decomposition error, and second is to make EMD insensitive to small variations in the analyzed signal. The proposed algorithm is applied to a gait signal and shown to consistently yield two IMFs, even in the presence of small disturbances.proposes to use selective extrema analysis while generating IMFs with two goals. One is to reduce/control the number of IMFs a signal is decomposed into with a small decomposition error, and second is to make EMD insensitive to small variations in the analyzed signal. The proposed algorithm is applied to a gait signal and shown to consistently yield two IMFs, even in the presence of small disturbances.