Jump Plus AM-FM Mode Decomposition

TY - JOUR

T1 - Jump Plus AM-FM Mode Decomposition

AU - Nazari, Mojtaba

AU - Korshj, Anders Rosendal

AU - Rehman, Naveed Ur

PY - 2025/1/31

Y1 - 2025/1/31

N2 - A novel approach for decomposing a nonstationary signal into amplitude- and frequency-modulated (AM-FM) oscillations and discontinuous (jump) components is proposed. Current nonstationary signal decomposition methods are designed to either obtain constituent AM-FM oscillatory modes or the discontinuous and residual components from the data, separately. Yet, many real-world signals of interest simultaneously exhibit both behaviors i.e., jumps and oscillations. Currently, no available method can extract jumps and AM-FM oscillatory components directly from the data. In our novel approach, we design and solve a variational optimization problem to accomplish this task. The optimization formulation includes a regularization term to minimize the bandwidth of all signal modes for effective oscillation modeling, and a prior for extracting the jump component. Our approach addresses the limitations of conventional AM-FM signal decomposition methods in extracting jumps and the limitations of existing jump extraction methods in decomposing multiscale oscillations. By employing an optimization framework that accounts for both multiscale oscillatory components and discontinuities, the proposed method shows superior performance compared to existing decomposition techniques. We demonstrate the effectiveness of our approach on synthetic, real-world, single-channel, and multivariate data, highlighting its utility in three specific applications: earth's electric field signals, electrocardiograms (ECG), and electroencephalograms (EEG).

AB - A novel approach for decomposing a nonstationary signal into amplitude- and frequency-modulated (AM-FM) oscillations and discontinuous (jump) components is proposed. Current nonstationary signal decomposition methods are designed to either obtain constituent AM-FM oscillatory modes or the discontinuous and residual components from the data, separately. Yet, many real-world signals of interest simultaneously exhibit both behaviors i.e., jumps and oscillations. Currently, no available method can extract jumps and AM-FM oscillatory components directly from the data. In our novel approach, we design and solve a variational optimization problem to accomplish this task. The optimization formulation includes a regularization term to minimize the bandwidth of all signal modes for effective oscillation modeling, and a prior for extracting the jump component. Our approach addresses the limitations of conventional AM-FM signal decomposition methods in extracting jumps and the limitations of existing jump extraction methods in decomposing multiscale oscillations. By employing an optimization framework that accounts for both multiscale oscillatory components and discontinuities, the proposed method shows superior performance compared to existing decomposition techniques. We demonstrate the effectiveness of our approach on synthetic, real-world, single-channel, and multivariate data, highlighting its utility in three specific applications: earth's electric field signals, electrocardiograms (ECG), and electroencephalograms (EEG).

KW - biomedical applications

KW - empirical mode decomposition

KW - jump extraction

KW - multivariate data

KW - Variational mode decomposition

UR - https://www.scopus.com/pages/publications/105001209716

U2 - 10.1109/TSP.2025.3535822

DO - 10.1109/TSP.2025.3535822

M3 - Journal article

SN - 1053-587X

VL - 73

SP - 1081

EP - 1093

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

ER -