Contactless Simultaneous Breathing and Heart Rate Detections in Physical Activity Using IR-UWB Radars.

Vital signs monitoring in physical activity (PA) is of great significance in daily healthcare. Impulse Radio Ultra-WideBand (IR-UWB) radar provides a contactless vital signs detection approach with advantages in range resolution and penetration. Several researches have verified the feasibility of IR-UWB radar monitoring when the target keeps still. However, various body movements are induced by PA, which lead to severe signal distortion and interfere vital signs extraction. To address this challenge, a novel joint chest-abdomen cardiopulmonary signal estimation approach is proposed to detect breath and heartbeat simultaneously using IR-UWB radars. The movements of target chest and abdomen are detected by two IR-UWB radars, respectively. Considering the signal overlapping of vital signs and body motion artifacts, Empirical Wavelet Transform (EWT) is applied on received radar signals to remove clutter and mitigate movement interference. Moreover, improved EWT with frequency segmentation refinement is applied on each radar to decompose vital signals of target chest and abdomen to vital sign-related sub-signals, respectively. After that, based on the thoracoabdominal movement correlation, cross-correlation functions are calculated among chest and abdomen sub-signals to estimate breath and heartbeat. The experiments are conducted under three kinds of PA situations and two general body movements, the results of which indicate the effectiveness and superiority of the proposed approach.

12-Lead ECG arrhythmia classification using cascaded convolutional neural network and expert feature.

Owing to widely available digital ECG data and recent advances in deep learning techniques, automatic ECG arrhythmia classification based on deep neural network has gained growing attention. However, existing neural networks are mainly validated on single­lead ECG, not involving the correlation and difference between multiple leads, while multiple leads ECG provides more complete description of the cardiac activity in different directions. This paper proposes a 12­lead ECG arrhythmia classification method using a cascaded convolutional neural network (CCNN) and expert features. The one-dimensional (1-D) CNN is firstly designed to extract features from each single­lead signal. Subsequently, considering the temporal correlation and spatial variability between multiple leads, features are cascaded as input to two-dimensional (2-D) densely connected ResNet blocks to classify the arrhythmia. Furthermore, features based on expert knowledge are extracted and a random forest is applied to get a classification probability. Results from both CCNN and expert features are combined using the stacking technique as the final classification result. The method has been validated against the first China ECG Intelligence Challenge, obtaining a final score of 86.5% for classifying 12­lead ECG data with multiple labels into 9 categories.

Body Orientation and Vital Sign Measurement With IR-UWB Radar Network.

Utilizing Impulse Radio Ultra-WideBand (IR-UWB) radar for vital sign monitoring has attracted growing interest due to the noncontact measurement without privacy concerns. Most of existing researches assume that the subject's chest is directed to the radar antenna, to ensure the strength of backscattered signals from chest movement. However, a large angle between the antenna and the subject's chest caused by the body orientation badly affects the monitoring accuracy. Multiple observations of the same cardiopulmonary activity from different orientations provide more available measurements. This paper addresses the challenge by using an IR-UWB radar network instead of a single radar. Three IR-UWB radars are placed as endpoints of an equilateral triangle to collect vital sign information of a subject sitting at the center. A Conditional Generative Adversarial Network (CGAN) method is proposed to fuse multisensory data. First, the body orientation is classified by combining signal features and a random forest classifier. Then the impact of different angles on vital sign monitoring results is discussed and validated in each orientation. The data fusion process is modelled as an extended generative network with orientation based condition to produce the enhanced vital signal. This signal is optimized with the discriminator network to a fitted sinusoidal wave with heartbeat and respiratory information. Experimental results on measuring Heartbeat Rate (HR) in different orientations reveal the effectiveness and stability of the proposed method.

MMT-HEAR: Multiple Moving Targets Heartbeats Estimation and Recovery Using IR-UWB Radars.

Populations around the world are rapidly ageing. Age-friendly environments address the significance of continuous inhome vital sign monitoring. Impulse Radio Ultra-WideBand (IR-UWB) radar serves as a household healthcare assistance, providing non-contact vital sign monitoring without privacy issues and illumination limitation. However, the body movements bring difficulty in extracting heartbeat from radar signals, let alone obtaining complete information with body occlusions among multiple targets. This paper proposes a Multiple Moving Targets Heartbeat Estimation And Recovery (MMT-HEAR) approach to extract vital signs using IR-UWB radars. CLEAN and Joint Probability Data Association (JPDA) algorithms are firstly performed on each radar to estimate target-to-antenna distances of multiple targets. Considering signal obstruction and attenuation for targets occluded by others, the location-based distance optimization is proposed to refine these distances by combining information from all radars. Then the mapping from signal amplitudes to refined distances is introduced and combined with the Variational Nonlinear Chirp Mode Decomposition (VNCMD) to extract vital signs with body movements. To the best of our knowledge, this is the first attempt to monitor vital signs of multiple moving targets with radars. The averaging accuracy for two moving targets heartbeat monitoring during a 20-minutes observation is 85.93% with MMT-HEAR. Compared to two other conventional methods, the MMT-HEAR approach yields improvements of 16.11% and 10.16%, revealing the efficiency and robustness of this proposed approach.

A PEGDA/DNA Hybrid Hydrogel for Cell-Free Protein Synthesis.

Cell-free protein synthesis (CFPS) has the advantage of rapid expression of proteins and has been widely implemented in synthetic biology and protein engineering. However, the critical problem limiting CFPS industrial application is its relatively high cost, which partly attributes to the overexpense of single-use DNA templates. Hydrogels provide a possible solution because they can preserve and reutilize the DNA templates in CFPS and have great potential in elevating the protein production yield of the CFPS. Here, we presented a low-cost hybrid hydrogel simply prepared with polyethylene glycol diacrylate (PEGDA) and DNA, which is capable of high-efficient and repeated protein synthesis in CFPS. Parameters governing protein production specific to hybrid hydrogels were optimized. Structures and physical properties of the hybrid hydrogel were characterized. Transcription and expression kinetics of solution phase system and gel phased systems were investigated. The results showed that PEGDA/DNA hydrogel can enhance the protein expression of the CFPS system and enable a repeated protein production for tens of times. This PEGDA/DNA hybrid hydrogel can serve as a recyclable gene carrier for either batch or continuous protein expression, and paves a path toward more powerful, scalable protein production and cell-free synthetic biology.

Structure-tuned membrane active Ir-complexed oligoarginine overcomes cancer cell drug resistance and triggers immune responses in mice.

The development of chemotherapy, an important cancer treatment modality, is hindered by the frequently found drug-resistance phenomenon. Meanwhile, researchers have been enthused lately by the synergistic use of chemotherapy with emerging immunotherapeutic treatments. In an effort to address both of the two unmet needs, reported herein is a study on a series of membrane active iridium(iii) complexed oligoarginine peptides with a new cell death mechanism capable of overcoming drug resistance as well as stimulating immunological responses. A systematic structure-activity relationship study elucidated the interdependent effects of three structural factors, i.e., hydrophobicity, topology and cationicity, on the regulation of the cytotoxicity of the Ir(iii)-oligoarginine peptides. With the most prominent toxicities, Ir-complexed octaarginines (R8) were found to display a progressive oncotic cell death featuring cell membrane-penetration and eruptive cytoplasmic content release. Consequently, this membrane-centric death mechanism showed promising potential in overcoming multiple chemical drug-resistance of cancer cells. More interestingly, the eruptive mode of cell death proved to be immunogenic by stimulating the dendritic cell maturation and inflammatory factor accumulation in mice tumours. Taking these mechanisms together, this work demonstrates that membrane active compounds may become the next generation chemotherapeutics because of their combined advantages.

Number and Angle Analysis in UWB Radar Deployment for Vital Sign Monitoring.

In recent years, more studies focus on the UltraWide Band (UWB) radar to provide a noncontact vital sign monitoring service. To further improve the accuracy of vital sign monitoring, the UWB radar network composed by multiple radars is considered for providing information on different angles. The radar deployment is a key factor in the radar network to impact the monitoring results, since that different deployments bring diverse combinations of vital sign information. To the best of our knowledge, few studies attempt to optimize the radar deployment for the purpose of improving the vital sign monitoring accuracy. This paper provides an analysis on the number and angle in radar deployment for vital sign monitoring. To firstly validate the superiority of utilizing multiple radars rather than the single radar, the theoretical analysis is performed by combining the vital sign monitoring model and the Cramer-Rao Low Bound (CRLB) theory. Then experiments for discussing the effect of the radar number and the angle between radars are conducted in realistic environment. Considering the radar number from 2 to 4, signals are acquired from radars located symmetrically with the subject sitting at the center. Additionally, the deployments of two radars with angles of 0°, 60°, 120°, 180°, 240° and 300° are discussed, ensuring that at least one radar is directed to the chest of the subject. A Neural Network (NN) based data fusion method is performed to obtain the fused vital signal from the radars. The accuracy of the NN is discussed as the evaluating indicators for these deployments.

HEAR: Approach for Heartbeat Monitoring with Body Movement Compensation by IR-UWB Radar.

Further applications of impulse radio ultra-wideband radar in mobile health are hindered by the difficulty in extracting such vital signals as heartbeats from moving targets. Although the empirical mode decomposition based method is applied in recovering waveforms of heartbeats and estimating heart rates, the instantaneous heart rate is not achievable. This paper proposes a Heartbeat Estimation And Recovery (HEAR) approach to expand the application to mobile scenarios and extract instantaneous heartbeats. Firstly, the HEAR approach acquires vital signals by mapping maximum echo amplitudes to the fast time delay and compensating large body movements. Secondly, HEAR adopts the variational nonlinear chirp mode decomposition in extracting instantaneous frequencies of heartbeats. Thirdly, HEAR extends the clutter removal method based on the wavelet decomposition with a two-parameter exponential threshold. Compared to heart rates simultaneously collected by electrocardiograms (ECG), HEAR achieves a minimum error rate 4.6% in moving state and 2.25% in resting state. The Bland⁻Altman analysis verifies the consistency of beat-to-beat intervals in ECG and extracted heartbeat signals with the mean deviation smaller than 0.1 s. It indicates that HEAR is practical in offering clinical diagnoses such as the heart rate variability analysis in mobile monitoring.