Abstract
Electrocardiography is the gold standard for electrical heartbeat activity, but offers no direct measurement of mechanical activity. Mechanical cardiac activity can be assessed non-invasively using, e.g., ballistocardiography and recently, medical radar has emerged as a contactless alternative modality. However, all modalities for measuring the mechanical cardiac activity are affected by respiratory movements, requiring a signal separation step before higher-level analysis can be performed. This paper adapts a non-linear filter for separating the respiratory and cardiac signal components of radar recordings. In addition, we present an adaptive algorithm for estimating the parameters for the non-linear filter. The novelty of our method lies in the combination of the non-linear signal separation method with a novel, adaptive parameter estimation method specifically designed for the non-linear signal separation method, eliminating the need for manual intervention and resulting in a fully adaptive algorithm. Using the two benchmark applications of (i) cardiac template extraction from radar and (ii) peak timing analysis, we demonstrate that the non-linear filter combined with adaptive parameter estimation delivers superior results compared to linear filtering. The results show that using locally projective adaptive signal separation (LoPASS), we are able to reduce the mean standard deviation of the cardiac template by at least a factor of 2 across all subjects. In addition, using LoPASS, 9 out of 10 subjects show significant (at a confidence level of 2.5%) correlation between the R-T-interval and the R-radar-interval, while using linear filters this ratio drops to 6 out of 10. Our analysis suggests that the improvement is due to better preservation of the cardiac signal morphology by the non-linear signal separation method. Hence, we expect that the non-linear signal separation method introduced in this paper will mostly benefit analysis methods investigating the cardiac radar signal morphology on a beat-to-beat basis.
Highlights
Electrocardiography (ECG) has become a universally accepted standard for measuring heart rate
In the second benchmark application, we look at the relationship between the timing of the major peaks in the cardiac radar signal measured with respect to the R-peak of the simultaneously recorded ECG and the R-T-interval
This paper aims to introduce locally projective adaptive signal separation (LoPASS), and to demonstrate that (i) it can be applied to medical radar recordings and that (ii) it outperforms linear filters on the benchmark applications reported here
Summary
Electrocardiography (ECG) has become a universally accepted standard for measuring heart rate. Research has been focused on unobtrusive measurements of mechanical heartbeat activity using, Locally Projective Adaptive Signal Separation e.g., ballistocardiography (BCG) or seismocardiography (Castiglioni et al, 2007; Postolache et al, 2007; Inan et al, 2009a; Pinheiro et al, 2010; Giovangrandi et al, 2011) Most of these projects aim to develop unobtrusive, long-term home monitoring systems for monitoring patients with cardiac conditions (Brüser et al, 2012, 2013; Christoph Hoog et al, 2015) or monitoring sleep quality (Paalasmaa et al, 2012, 2014). The cardiac signal itself can contain high frequency components due to the influence of heart sounds (Castiglioni et al, 2011), which makes it difficult to reduce noise using simple low-pass filtering (Yao et al, 2012) For these reasons, we have previously adapted a non-linear algorithm to separate the respiratory and cardiac components from BCG recordings obtained from bed-mounted sensors (Yao et al, 2014). We have shown that this non-linear method achieves superior results compared to linear filters
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