Extracting Cardiac Information From Medical Radar Using Locally Projective Adaptive Signal Separation

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.

Contactless Real-Time Heartbeat Detection via 24 GHz Continuous-Wave Doppler Radar Using Artificial Neural Networks

“…Many previous works considered that the heartbeat-related displacements could be modeled as a sine wave [16], half-sine pulses [21], Gaussian pulses [22], or as an array of two consecutive pulses [24]. However, the mechanical response of the chest wall has a complex waveform that is difficult to model [33]. Figure 3b shows the heartbeat-related displacement obtained from the radar data shown in Figure 3a, using the extended differentiate and cross-multiply (DACM) demodulation algorithm Figure 3a shows a fragment of recorded time-aligned ECG and radar signals.…”

“…Radar sensors are used for the detection of sub-millimeter movements of chest wall skin surface that occur due to heartbeats, whereas various signal processing methods are employed for heart rate extraction from discretized radar signals. Radar technology has shown not only great potential for heart rate estimation but also the potential for extracting ventricular ejection timing using nonlinear filtering methods [33].…”