Empirical　wavelet　transform　(EWT)　can　successfully　decompose　a　smooth　or　noise-free　simulated　signal　into　several　components.　This　method　has　encountered　difficulties　in　processing　simulated　signals　containing　noise　and　in　applications.　In　order　to　improve　the　shortcomings　of　EWT　and　expand　its　application,　this　paper　proposes　a　variable　spectral　segmentation　EWT　(VEWT)　associated　with　the　trend　of　spectral　fluctuation.　Different　from　scale-space　representation　and　other　optimized　direct　segmentation　methods,　this　paper　proposes　a　selective　segmentation　method.　The　extreme　points　of　Multi-taper　power　spectral　density　(MPSD)　are　used　to　estimate　the　modes.　An　extended　algorithm　is　proposed　on　the　basis　of　Levenberg-Marquardt-Fletcher,　which　uses　the　extreme　points　and　positions　of　MPSD　to　calculate　the　bandwidth　corresponding　to　each　mode.　The　information　in　each　frequency　band　will　be　determined　as　the　final　mode.　In　the　process　of　loop　extraction,　a　set　of　boundaries　associated　with　the　fluctuations　of　the　spectral　will　be　obtained.　The　proposed　method　is　more　advantageous　for　the　decomposition　of　signals　containing　noise.　Simulated　signals　are　used　to　verify　the　effectiveness　of　the　proposed　method.　In　addition,　the　MIT-BIH　Arrhythmia　Database　is　used　to　verify　the　applicability　of　the　proposed　method.　(C)　2021　Elsevier　Inc.　All　rights　reserved.