Fully Adaptive Time-Varying Wave-Shape Model: Applications in Biomedical Signal Processing

TL;DR

This paper introduces a novel time-varying wave-shape extraction algorithm based on an adaptive non-harmonic model, effectively handling non-stationary biomedical signals for denoising, decomposition, and segmentation with high accuracy.

Contribution

It presents a new adaptive wave-shape extraction method that improves accuracy in denoising and decomposition of non-stationary signals over existing techniques.

Findings

Accurately recovers wave-shape in noisy signals

Outperforms existing denoising algorithms

Effectively segments biomedical signals

Abstract

In this work, we propose a time-varying wave-shape extraction algorithm based on a modified version of the adaptive non-harmonic model for non-stationary signals. The model codifies the time-varying wave-shape information in the relative amplitude and phase of the harmonic components of the wave-shape. The algorithm was validated on both real and synthetic signals for the tasks of denoising, decomposition and adaptive segmentation. For the denoising task, both monocomponent and multicomponent synthetic signals were considered. In both cases, the proposed algorithm can accurately recover the time-varying wave-shape of non-stationary signals, even in the presence of high levels of noise, outperforming existing wave-shape estimation algorithms and denoising methods based on short-time Fourier transform thresholding. The denoising of an electroencephalograph signal was also performed, giving similar results. For decomposition, our proposal was able to recover the composing waveforms more accurately by considering the time variations from the harmonic amplitude functions when compared to existing methods. Finally, the algorithm was used for the adaptive segmentation of synthetic signals and an electrocardiograph of a patient undergoing ventricular fibrillation.