Non-contact monitoring of heart rate variability using medical radar for the evaluation of dynamic changes in autonomic nervous activity during a head-up tilt test

Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System

An increasing number of studies on non-contact vital sign detection using radar are now beginning to turn to data-driven neural network approaches rather than traditional signal-processing methods. However, there are few radar datasets available for deep learning due to the difficulty of acquiring and labeling the data, which require specialized equipment and physician collaboration. This paper presents a new model of heartbeat-induced chest wall motion (CWM) with the goal of generating a large amount of simulation data to support deep learning methods. An in-depth analysis of published CWM data collected by the VICON Infrared (IR) motion capture system and continuous wave (CW) radar system during respiratory hold was used to summarize the motion characteristics of each stage within a cardiac cycle. In combination with the physiological properties of the heartbeat, appropriate mathematical functions were selected to describe these movement properties. The model produced simulation data that closely matched the measured data as evaluated by dynamic time warping (DTW) and the root-mean-squared error (RMSE). By adjusting the model parameters, the heartbeat signals of different individuals were simulated. This will accelerate the application of data-driven deep learning methods in radar-based non-contact vital sign detection research and further advance the field.

Proof-of-principle Experiment on 24 GHz Medical Radar for Non-contact Vital Signs Measurement

Medical radar for non-contact vital signs measurement exhibits great potential in both clinical and home healthcare settings. Especially during the corona virus spreading time, non-contact sensing more clearly shows the advantages. Many previous studies have concentrated on medical radar-based healthcare applications, but pay less attention to the working principles. A clear understanding of medical radars at both the mathematical and physical levels is critically important for developing application-specific signal processing algorithms. Therefore, this study aims to re-define the operating principle of radar, and a proof-of-principle experiment was performed on both actuator and human subjects using 24 GHz Doppler radar system. Experimental results indicate that there is a difference in the radar output signals between the two cases, where the displacement is greater than and less than half of the wavelength. For the former situation, the displacement x = n.λ/2 (n ≥ 1), one peak of radar signals corresponds to n peaks of baseband signals. By contrast, for the latter situation, the displacement x < λ/2, one peak of radar signals corresponds to one peak of baseband signals. Strikingly, with human measurement on the dorsal side, the the number of respiration peaks are seen from the radar raw signals.

Contactless analysis of heart rate variability during cold pressor test using radar interferometry and bidirectional LSTM networks

Contactless measurement of heart rate variability (HRV), which reflects changes of the autonomic nervous system (ANS) and provides crucial information on the health status of a person, would provide great benefits for both patients and doctors during prevention and aftercare. However, gold standard devices to record the HRV, such as the electrocardiograph, have the common disadvantage that they need permanent skin contact with the patient. Being connected to a monitoring device by cable reduces the mobility, comfort, and compliance by patients. Here, we present a contactless approach using a 24 GHz Six-Port-based radar system and an LSTM network for radar heart sound segmentation. The best scores are obtained using a two-layer bidirectional LSTM architecture. To verify the performance of the proposed system not only in a static measurement scenario but also during a dynamic change of HRV parameters, a stimulation of the ANS through a cold pressor test is integrated in the study design. A total of 638 minutes of data is gathered from 25 test subjects and is analysed extensively. High F-scores of over 95% are achieved for heartbeat detection. HRV indices such as HF norm are extracted with relative errors around 5%. Our proposed approach is capable to perform contactless and convenient HRV monitoring and is therefore suitable for long-term recordings in clinical environments and home-care scenarios.