Novel approach for ECG separation using adaptive constrained IVABMGGMM

Abstract

In this paper, we introduce the constrained independent vector analysis integrated with the bounded multivariate generalized Gaussian mixture model (cIVABMGGMM) to tackle the limitations of independent component analysis (ICA) when applied to multivariate data, accompanied by its adaptive version, the acIVABMGGMM, aimed at alleviating associated constraints. The acIVABMGGMM employs a full covariance matrix that considers feature correlations, effectively addressing the challenges posed by ICA and independent vector analysis (IVA) models when analyzing multivariate data. The innovative acIVABMGGMM framework merges the adaptability inherent in data-driven methods with the capability to manage noise and other artifacts often encountered in model-based approaches. This technique effectively employs prior knowledge to guide the solution, avoiding the imposition of inaccurate constraints. To overcome these challenges, our set of two constrained methods incorporates prior source information into the IVA model, effectively mitigating its limitations in data with a high number of sources. We assess the efficacy of our proposed models through three distinct ECG separation experiments, which include heartbeat separation, fetal ECG extraction, and arrhythmia detection. Notably, the performance metrics demonstrate the superiority of our models over the baseline approaches in the conducted experiments.