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.

经验小波变换 (EWT) 可以成功地将平滑或无噪声的模拟信号分解为多个分量。这种方法在处理包含噪声的模拟信号和应用中遇到了困难。为了改善EWT的缺点并扩大其应用，本文提出了一种与光谱波动趋势相关的可变光谱分割EWT（VEWT）。与尺度空间表示和其他优化的直接分割方法不同，本文提出了一种选择性分割方法。多锥功率谱密度 (MPSD) 的极值点用于估计模式。在Levenberg-Marquardt-Fletcher的基础上提出了一种扩展算法，利用MPSD的极值点和位置来计算每种模式对应的带宽。每个频带中的信息将被确定为最终模式。在循环提取的过程中，会得到一组与频谱波动相关的边界。所提出的方法更有利于分解含有噪声的信号。仿真信号用于验证所提出方法的有效性。此外，MIT-BIH 心律失常数据库用于验证所提出方法的适用性。