Coorbit Theory, Multi-α-Modulation Frames, and the Concept of Joint Sparsity for Medical Multichannel Data Analysis

This paper is concerned with the analysis and decomposition of medical multichannel data. We present a signal processing technique that reliably detects and separates signal components such as mMCG, fMCG, or MMG by involving the spatiotemporal morphology of the data provided by the multisensor geometry of the so-called multichannel superconducting quantum interference device (SQUID) system. The mathematical building blocks are coorbit theory, multi-α-modulation frames, and the concept of joint sparsity measures. Combining the ingredients, we end up with an iterative procedure (with component-dependent projection operations) that delivers the individual signal components.