Unifying Adaptive Fourier and M\"obius-Based Models for Efficient and Interpretable Biomedical Signal Decomposition

Oscillatory biomedical signals such as electrocardiograms (ECG) and electroencephalograms (EEG) call for decompositions that are both computationally efficient and interpretable.

This paper establishes a formal connection between two finite-order frameworks that have largely evolved independently: Adaptive Fourier Decomposition (AFD), based on orthonormal Takenaka-Malmquist expansions, and the Frequency-Modulated Mobius (FMM) model, a parametric decomposition built on Mobius transforms with morphologically meaningful parameters.

We prove that finite-order AFD and FMM decompositions are mathematically equivalent.

Under mild regularity assumptions, we further show that their associated estimation procedures solve the same underlying optimization problem when FMM is formulated with independent Gaussian noise.

The results are extended to multi-channel signals, which are central in multilead bioelectric recordings.

Practically, the equivalence clarifies how fast AFD approximations, including FFT-based implementations, relate to FMM-style parametrization and component interpretability.

We illustrate these implications with an EEG example evaluating approximation behavior as the number of components increases, and with an ECG use case comparing five-component decompositions on representative beats, contrasting unlabeled AFD components with physiologically identified FMM components.

Overall, the proposed equivalence provides a principled basis to leverage the computational advantages of AFD alongside the interpretability of FMM in biomedical signal analysis.