A Low-Cost Flexible Perforated Respiratory Sensor Based on Platinum for Continuous Respiratory Monitoring

Editorial for the Special Issue on Wearable and Implantable Bio-MEMS Devices and Applications

Wearable and implantable bio-MEMS sensors and actuators have attracted tremendous attention in the fields of health monitoring, disease treatment, and human-machine interaction, to name but a few [...].

Crosstalk-free graphene-liquid elastomer based printed sensors for unobtrusive respiratory monitoring

Flexible strain sensors have garnered attraction in the human healthcare domain. However, caveats like crosstalk and noise associated with the output signal of such a sensor often limit the accuracy. Hence, developing a strain sensor via frugal engineering is critical, thereby warranting its mass utility. A stencil printable graphene/liquid elastomeric crosstalk-free strain sensor for unobtrusive respiratory monitoring is reported herein. Printing supports the frugality of the process and avoids complex fabrication. The sensor was mounted on a wearable mask, and the sensor console was fabricated. The console demonstrated the capability to detect the respiratory profile at room and low temperature (-26 °C) with an SNR of -12.85 dB. Developed sensors could nullify the impact of temperature and humidity and generate respiratory signals due to strain induced by breathing. A model experiment was conducted to support the fidelity of the strain mechanism. The console demonstrated excellent stability (over 500 cycles) with a sensitivity of -196.56 (0-0.17% strain) and 117.49 (0.17-0.34% strain). The console could accurately determine conditions like eupnea, tachypnoea, etc., and transmit the data wirelessly via Bluetooth. These findings solve major caveats in flexible sensor development by focusing on selectivity, sensitivity, and stability.

Fully Integrated Patch Based on Lamellar Porous Film Assisted GaN Optopairs for Wireless Intelligent Respiratory Monitoring

Respiratory pattern is one of the most crucial indicators for accessing human health, but there has been limited success in implementing fast-responsive, affordable, and miniaturized platforms with the capability for smart recognition. Herein, a fully integrated and flexible patch for wireless intelligent respiratory monitoring based on a lamellar porous film functionalized GaN optoelectronic chip with a desirable response to relative humidity (RH) variation is reported. The submillimeter-sized GaN device exhibits a high sensitivity of 13.2 nA/%RH at 2-70%RH and 61.5 nA/%RH at 70-90%RH, and a fast response/recovery time of 12.5 s/6 s. With the integration of a wireless data transmission module and the assistance of machine learning based on 1-D convolutional neural networks, seven breathing patterns are identified with an overall classification accuracy of >96%. This integrated and flexible on-mask sensing platform successfully demonstrates real-time and intelligent respiratory monitoring capability, showing great promise for practical healthcare applications.

Changes in the autonomic cardiorespiratory activity in parturient women with severe and moderate features of preeclampsia

Background

Cardiorespiratory coupling (CRC) is a physiological phenomenon that reflects the mutual interaction between the cardiac and respiratory control systems. It is mainly associated with efferent vagal activity from the central autonomic network. Few studies have explored the autonomic changes of CRC in preeclampsia, a critical obstetric complication related to possible autonomic dysfunctions and inflammatory disturbances. This study examined the autonomic mechanisms of CRC in women with severe and moderate preeclampsia and healthy controls by applying nonlinear methods based on information theory, such as mutual information (MI) and Renyi's mutual information (RMI) and the linear and nonlinear analysis of the Pulse-Respiration Quotient (PRQ).

Methods

We studied three groups of parturient women in the third trimester of pregnancy with a clinical diagnosis of preeclampsia without severe symptoms (P, 38.5 ± 1.4 weeks of pregnancy, n=19), preeclampsia with severe symptoms (SP, 37.5 ± 0.9 weeks of pregnancy, n=22), and normotensive control women (C, 39.1 ± 1.3 weeks of pregnancy, n=20). 10-minutes of abdominal electrocardiograms (ECG) and respiratory signals (RESP) were recorded in all the participants. Subsequently, we obtained the maternal beat-to-beat (RR) and breath-to-breath (BB) time series from ECG and RESP, respectively. The CRC between RR and BB was quantified by nonlinear methods based on information theory, such as MI and RMI, along with the analysis of the novel index of PRQ. Subsequently, we computed the mean PRQ (mPRQ) and the normalized permutation entropy (nPermEn_PRQ) from the PRQ time series generated from BB and RR. In addition, we examined the vagal activity in the three groups by the logarithm of the median of the distribution of the absolute values of successive RR differences (logRSA).

Results

The MI and RMI values were significantly lower (p<0.05) in the preeclamptic groups compared to the control group. However, no significant differences were found between the preeclamptic groups. The logRSA and nPermEn_PRQ indices were significantly lower (p<0.05) in SP compared to C and P.

Conclusion

Our data suggest that parturient women with severe and mild preeclampsia may manifest an altered cardiorespiratory coupling compared with normotensive control women. Disrupted CRC in severe preeclampsia could be associated with vagal withdrawal and less complex cardiorespiratory dynamics. The difference in vagal activity between the preeclamptic groups may suggest a further reduction in vagal activity associated with the severity of the disease.