Decomposed Mean Euler-Poincaré Characteristic Model for a Non-Gaussian Physiological Random Field

This paper introduces a new approach of the mean Euler-Poincaré characteristic for nonGaussian random fields (NGRF), which is based on the decomposition by a basic function named motherwave. The method is proved for long-term recorded, noisy physiological signals. A pretreatment allows the signal to become smooth as the original one is fitted through a Random Algebraic Polynomials (RAP)-based scheme. After that, the polynomized signals are merged by thresholding the RAP function at different levels u. In this way, it is formed a real-valued non-Gaussian physiological random field (NGPRF). Thereby, we deal with their geometric properties centered on their excursion sets A_u(Φ, T) and a topological invariant, such as the Euler Poincaré Characteristic (EPC) φ(Au(Φ, T)). The highlight of this work is an explicit model, referred to as the decomposed mean Euler-Poincaré characteristic (DMEPC). The proposed method produces a reduced model with a viable interpretation for different heart conditions investigated for data issued from Holter EKG recordings.

Abstract licence: CC BY-NC-ND

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