Endpoint Extension in Empirical Mode Decomposition for Hilbert-Huang Transform

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Endpoint Extension Techniques for Empirical Mode Decomposition (EMD) in Hilbert-Huang Transform Implementation

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This article explores the implementation of endpoint extension methods during the Empirical Mode Decomposition (EMD) phase of the Hilbert-Huang Transform (HHT). When performing EMD on time series data, endpoint effects can cause distortion in the extracted Intrinsic Mode Functions (IMFs). The endpoint extension technique addresses this by artificially extending signal boundaries before decomposition. Common implementation approaches include mirror extension, where signals are reflected at endpoints, or predictive extension using algorithms like AR model forecasting. In MATLAB implementations, this typically involves creating wrapper functions that preprocess input signals using techniques such as fliplr() for mirroring or ar() for autoregressive prediction before passing data to the EMD routine. Proper endpoint handling ensures more stable IMF extraction and improves the subsequent Hilbert spectral analysis accuracy.

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