IEEE Trans Biomed Eng. 2020 May;67(5):1377-1386. doi: 10.1109/TBME.2019.2936943. Epub 2019 Aug 22.
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.
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.
The performance of the proposed method is assessed in presence of white and colored noise, in different signal-to-noise ratios.
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.
在生物医学应用中，混合暂时非平稳信号非常常见。源信号的非平稳性可用作信号分离的判别特性。本文提出了一种半盲源分离算法，用于从线性多通道信号和噪声混合中提取暂时非平稳分量。
通过使用专门的指标来监测新息过程的一阶和二阶统计量，提出了一种用于检测和融合暂时非平稳事件的假设检验。作为概念验证，使用两种类型的非平稳性检测器（1）局部功率变化检测器和 2）使用扩展卡尔曼滤波器的新息过程特性的模型偏差检测器，对从母体腹部获取的非侵入性胎儿心脏记录的公共可用数据集进行了定制和测试。
在存在白噪声和有色噪声的情况下，在不同的信噪比下评估了所提出方法的性能。
所提出的方案是通用的，可用于从多元数据中的假定模型中提取非平稳事件和样本偏差，这是许多机器学习应用中的常见问题。
