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

Remote sensing of vital signs by medical radar time-series signal using cardiac peak extraction and adaptive peak detection algorithm: Performance validation on healthy adults and application to neonatal monitoring at an NICU

BACKGROUND AND OBJECTIVE

Continuous monitoring of vital signs plays a pivotal role in neonatal intensive care units (NICUs). In this paper, we present a system for monitoring fully non-contact medical radar-based vital signs to measure the respiratory rate (RR), heart rate (HR), I:E ratio, and heart rate variability (HRV). In addition, we evaluated its performance in a physiological laboratory and examined its adaptability in an NICU.

METHODS

A non-contact medical radar-based vital sign monitoring system that includes 24 GHz radar installed in an incubator was developed. To enable reliable monitoring, an advanced signal processing algorithm (i.e., a nonlinear filter to separate respiration and heartbeat signals from the output of radar), template matching to extract cardiac peaks, and an adaptive peak detection algorithm to estimate cardiac peaks in time-series were proposed and implemented in the system. Nine healthy subjects comprising five males and four females (24 ± 5 years) participated in the laboratory test. To evaluate the adaptability of the system in an NICU setting, we tested it with three hospitalized infants, including two neonates.

RESULTS

The results indicate strong agreement in healthy subjects between the non-contact system and reference contact devices for RR, HR, and inter-beat interval (IBI) measurement, with correlation coefficients of 0.83, 0.96, and 0.94, respectively. As anticipated, the template matching and adaptive peak detection algorithms outperformed the conventional approach. These showed a more accurate IBI close to the reference Bland-Altman analysis (proposed: bias of -3 ms, and 95% limits of agreement ranging from -73 to 67 ms; conventional: bias of -11 ms, and 95% limits of agreement ranging from -229 to 207 ms). Moreover, in the NICU clinical setting, the IBI correlation coefficient and 95% limit of agreement in the conventional method are 0.31 and 91 ms. The corresponding values obtained using the proposed method are 0.93 and 21 ms.

CONCLUSION

The proposed system introduces a novel approach for NICU monitoring using a non-contact medical radar sensor. The signal processing method combining cardiac peak extraction algorithm with the adaptive peak detection algorithm shows high adaptability in detecting IBI the time series in various application settings.

Design and Evaluation of Digital Filters for Non-Contact Measuring of HRV using Medical Radar and Its Application in Bedside Patient Monitoring System

A non-contact bedside monitoring system using medical radar is expected to be applied to clinical fields. Our previous studies have developed a monitoring system based on medical radar for measuring respiratory rate (RR) and heart rate (HR). Heart rate variability (HRV), which is essentially implemented in advanced monitoring system, such as prognosis prediction, is a more challenging biological information than the RR and HR. In this study, we designed a HRV measurement filter and proposed a method to evaluate the optimal cardiac signal extraction filter for HRV measurement. Because the cardiac component in the radar signal is much smaller than the respiratory component, it is necessary to extract the cardiac element from the radar output signal using digital filters. It depends on the characteristics of the filter whether the HRV information is kept in the extracted cardiac signal or not. A cardiac signal extraction filter that is not distorted in the time domain and does not miss the cardiac component must be adopted. Therefore, we focused on evaluating the interval between the R-peak of the electrocardiogram (ECG) and the radar-cardio peak of the cardiac signal measured by radar (R-radar interval). This is based on the fact that the time between heart depolarization and ventricular contraction is measured as the R-radar interval. A band-pass filter (BPF) with several bandwidths and a nonlinear filter, locally projective adaptive signal separation (LoPASS), were analyzed and compared. The optimal filter was quantitatively evaluated by analyzing the distribution and standard deviation of the R-radar intervals. The performance of this monitoring system was evaluated in elderly patient at the Yokohama Hospital, Japan.

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

The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from signs of life to complex mental states. The measurement of the ECG relies on electrodes attached to the skin to acquire the electrical activity of the heart, which imposes certain limitations. Recently, due to the advancement of wireless technology, it has become possible to pick up heart activity in a contactless manner. Among the possible ways to wirelessly obtain information related to heart activity, methods based on mm-wave radars proved to be the most accurate in detecting the small mechanical oscillations of the human chest resulting from heartbeats. In this paper, we presented a method based on a continuous-wave Doppler radar coupled with an artificial neural network (ANN) to detect heartbeats as individual events. To keep the method computationally simple, the ANN took the raw radar signal as input, while the output was minimally processed, ensuring low latency operation (<1 s). The performance of the proposed method was evaluated with respect to an ECG reference ("ground truth") in an experiment involving 21 healthy volunteers, who were sitting on a cushioned seat and were refrained from making excessive body movements. The results indicated that the presented approach is viable for the fast detection of individual heartbeats without heavy signal preprocessing.

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

Electrocardiography (ECG) is a mandatory standard for monitoring electrical activity of the heart in many clinical settings such as intensive care and emergency units. However, in situations wherein the skin is damaged, such as acute burn injuries, it is impossible to efficiently attach electrodes to the skin. In this study, we developed a non-contact cardiac monitoring system using a 24-GHz medical radar for directly measuring the beat-to-beat heart mechanical activity at a distance from a subject. The heart rate variability (HRV) was analysed using an autoregressive model (AR) from the measured beat-to-beat intervals during a head-up tilt test. To investigate the feasibility of the proposed system, we compared medical radar and ECG recording by using Lin's correlation coefficient and Bland-Altman analysis, which showed a negligible mean difference from the substantial agreement of Lin's correlation coefficient of 0.9 between the radar and ECG. The non-contact radar clearly monitored dynamic changes in HRV indices induced by the head-up tilt test. This type of non-contact HRV-sensing technique as an alternative approach has significant potential for advancing personal healthcare in both clinical and out-of-hospital settings.