Temporally Nonstationary Component Analysis; Application to Noninvasive Fetal Electrocardiogram Extraction

TL;DR

This paper introduces a semi-blind source separation method leveraging nonstationarity for extracting fetal ECG signals from multichannel recordings, demonstrating robustness against noise and model deviations.

Contribution

A novel framework utilizing nonstationarity detection and hypothesis testing for separating temporally nonstationary signals in biomedical data.

Findings

Effective in noisy environments with white and colored noise

Successfully applied to noninvasive fetal ECG extraction

Generalizable to other multivariate nonstationary signal analysis

Abstract

Objective: Mixtures of temporally nonstationary signals are very common in biomedical applications. The nonstationarity of the source signals can be used as a discriminative property for signal separation. Herein, a semi-blind source separation algorithm is proposed for the extraction of temporally nonstationary components from linear multichannel mixtures of signals and noises. Methods: A hypothesis test is proposed for the detection and fusion of temporally nonstationary events, by using ad hoc indexes for monitoring the first and second order statistics of the innovation process. As proof of concept, the general framework is customized and tested over noninvasive fetal cardiac recordings acquired from the maternal abdomen, over publicly available datasets, using two types of nonstationarity detectors: 1) a local power variations detector, and 2) a model-deviations detector using the innovation process properties of an extended Kalman filter. Results: The performance of the proposed method is assessed in presence of white and colored noise, in different signal-to-noise ratios. Conclusion and Significance: The proposed scheme is general and it can be used for the extraction of nonstationary events and sample deviations from a presumed model in multivariate data, which is a recurrent problem in many machine learning applications.