Accurate Estimation of Heart and Respiration Rates Based on an Optical Fiber Sensor Using Adaptive Regulations and Statistical Classifications Spectrum Analysis.

The aim of this work is to present a method for accurately estimating heart and respiration rates under different actual conditions based on a mattress which was integrated with an optical fiber sensor. During the estimation, a ballistocardiogram (BCG) signal, which was obtained from the optical fiber sensor, was used for extracting the heart rate and the respiration rate. However, due to the detrimental effects of the differential detector, self-interference, and variation of installation status of the sensor, the ballistocardiogram (BCG) signal was difficult to detect. In order to resolve the potential concerns of individual differences and body interferences, adaptive regulations and statistical classifications spectrum analysis were used in this paper. Experiments were carried out to quantify heart and respiration rates of healthy volunteers under different breathing and posture conditions. From the experimental results, it could be concluded that (1) the heart rates of 40-150 beats per minute (bpm) and respiration rates of 10-20 breaths per minute (bpm) were measured for individual differences; (2) for the same individuals under four different posture contacts, the mean errors of heart rates were separately 1.60 ± 0.98 bpm, 1.94 ± 0.83 bpm, 1.24 ± 0.59 bpm, and 1.06 ± 0.62 bpm, in contrast, the mean errors of the polar beat device were 1.09 ± 0.96 bpm, 1.44 ± 0.99 bpm, and 1.78 ± 0.94 bpm. Furthermore, the experimental results were validated by conventional counterparts which used skin-contacting electrodes as their measurements. It was reported that the heart rate was 0.26 ± 2.80 bpm in 95% confidence intervals (± 1.96SD) in comparison with Philips sure-signs VM6 medical monitor, and the respiration rate was 0.41 ± 1.49 bpm in 95% confidence intervals (± 1.96SD) in comparison with ECG-derived respiratory (EDR) measurements for respiration rates. It was indicated that the developed system using adaptive regulations and statistical classifications spectrum analysis performed better and could easily be used under complex environments.

Schottky Contacts Regularized Linear Regression for Signal Inconsistency Circumvent in Resistive Gas Micro-Nanosensors.

In the frontier resistive micro-nano gas sensors, the change rate reliability between the measured quantity and output signals has long been puzzled by the ineluctable device-to-device and run-to-run disparities. Here, a neotype sensing data interpretation method to circumvent these signal inconsistencies is reported. The method is based on discovery of a strong linear relation between the initial resistance in air (Ra ) and the absolute change in resistance after exposure to target gas (Ra -Rg ). Metal oxide gas sensors based on a micro-hot-plate are employed as the model system. The study finds that such correlation has a wide universality, even for devices incorporated with different sensing materials or under different gas atmosphere. Furthermore, this rule can also be extensible to graphene-based interdigital microelectrode. In situ probe scanning analyses illuminate that the linear dependence is closely related to work function matching level between metal electrode and sensitive layer. The Schottky barrier at metal-semiconductor junctions is the prominent parameter, whose height (ϕB ) can fundamentally impact material/electrode contact resistance, thereby further affecting the realistic nature expression of sensing materials. Using this correlation, a calibration procedure is proposed and embed in a fully integrated pocket-size sensor prototype, whose response outcomes demonstrated high credibility as compared to commercial apparatus.

Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records.

OBJECTIVES: Sepsis is caused by infection and subsequent overreaction of immune system and will severely threaten human life. The early prediction is important for the treatment of sepsis. This report aims to develop an early prediction method for sepsis 6 hours ahead on the basis of clinical electronic health records. DATA SOURCES: Challenge data are released by PhysioNet/Computing in Cardiology Challenge 2019 and obtained from ICU patients in three separate hospital systems. Part of the data from two datasets, including 40,336 subjects, are publicly available, and the remaining are used as hidden test set. A normalized utility score defined by the organizing committee is used for model performance evaluation. STUDY SELECTION: The supervised machine learning is applied to tackle this challenge. Specifically, we establish the prediction model under the framework of ensemble learning by integrating the artificial features based on clinical prior knowledge of sepsis with deep features automatically extracted by long short-term memory neural network. DATA EXTRACTION: Forty clinical variables, including eight vital signs, 26 laboratory values, and six demographics, were measured and recorded once an hour for each individual, and the binary label (0 or 1) was simultaneously provided for each item. DATA SYNTHESIS: The proposed model was evaluated by 30-fold cross-validation. The sensitivity, specificity, and normalized utility score were 0.641 ± 0.022, 0.844 ± 0.007, and 0.401 ± 0.019 on publicly available datasets, respectively. The final normalized utility score our team (UCAS_DataMiner) has obtained was 0.313 on full hidden test set (0.406, 0.373, and -0.215 on test set A, B, and C, respectively). CONCLUSIONS: We realized a 6-hour ahead early-onset prediction of sepsis on the basis of clinical electronic health record by ensemble learning. The results indicated the proposed model functioned well in the early prediction of sepsis. In particular, ensemble learning had a significant (p < 0.01) improvement than any single model in performance.

High Accuracy and Miniature 2-D Wind Sensor for Boundary Layer Meteorological Observation.

Wind speed and direction are important parameters in meteorological observation. A solid wind sensor is needed with a small quadcopter for boundary layer meteorological observation. In this paper, the principle of a cylindrical two-dimensional wind sensor is reported and the data from wind tunnel experiments are analyzed. A model is proposed to describe the distribution of the pressure difference across a diameter of a cylinder, and the wind sensor is fabricated with MEMS (Micro-Electro-Mechanical System) differential pressure sensors. The wind sensor cylinder has a small size with a diameter of 30 mm and a height of 80 mm. In wind tunnel tests in the range of 1 to 40 m/s, the relative speed measuring errors and the direction measuring errors of the prototype are no more than ± (0.2 + 0.03 V) m/s (V is standard wind speed) and 5°, respectively. An inclination angle model is proposed to correct the influence of tilt angle on the quadcopter platform, the wind sensor can maintain the original wind speed and direction measurement accuracy within the 30° inclination range after compensation.

Predicting forced vital capacity (FVC) using support vector regression (SVR).

OBJECTIVE: Spirometry, as the gold standard approach in the diagnosis of chronic obstructive pulmonary disease (COPD), has strict end of test (EOT) criteria (e.g. complete exhalation), which cannot be met by patients with compromised health states. Thus, significant parameters measured by spirometry, such as forced vital capacity (FVC), have limited accuracies. To address this issue, the present study aimed to develop models based on support vector regression (SVR) to predict values of FVC under the condition that the EOT criteria were not fully met. APPROACH: The prediction models for the quantification of FVC were developed based on SVR. A total of 354 subjects underwent conventional spirometry (CS), and the resulting data of forced expiratory volumes in 1 s (FEV1), peak expiratory flow (PEF), age and gender were used as input features, while the resulting values of the FVC were used as the target feature in the prediction models. Next, three prediction models (mixed model, normal model and abnormal model) were established according to the criterion in the diagnosis of COPD that a postbronchodilator shows an FEV1/FVC ratio lower than 0.70. Then, 35 subjects were recruited to be tested using both CS and a low-degree-of-EOT criteria spirometry (LDCS), which did not fully meet the EOT criteria of CS. In LDCS, subjects were allowed to terminate the procedure at their own will at any time after the technicians had assumed that both acceptable values of FEV1 and PEF had been obtained. Quantified values of FVC derived from both CS and LDCS were compared to validate the performances of the developed prediction models. MAIN RESULTS: The FVC prediction performances of the normal model and abnormal model were better than that of the mixed model. The root mean squared error are lower than 0.35 l and the accuracies are higher up to 95%. One-tailed t test results demonstrate that the absolute differences in the measured and predicted values are not significantly different from 0.15 l for both the abnormal model and the normal model. SIGNIFICANCE: Our study shows the possibility of predicting FVC with acceptable precision in cases where the EOT criteria of spirometry were not fully met, which can be beneficial for patients who cannot or did not achieve full exhalation in spirometry.

Expression of DCX and Transcription Factor Profiling in Photothrombosis-Induced Focal Ischemia in Mice.

Adult neurogenesis is present in the dentate gyrus and the subventricular zone in mammalian brain under physiological conditions. Recently, adult neurogenesis has also been reported in other brain regions after brain injury. In this study, we established a focal striatal ischemic model in adult mice via photothrombosis (PT) and investigated how focal ischemia elicits neurogenesis in the striatum. We found that astrocytes and microglia increased in early post-ischemic stage, followed by a 1-week late-onset of doublecortin (DCX) expression in the striatum. The number of DCX-positive neurons reached the peak level at day 7, but they were still observed at day 28 post-ischemia. Moreover, Rbp-J (a key effector of Notch signaling) deletion in astrocytes has been reported to promote the neuron regeneration after brain ischemia, and we provided the change of gene expression profile in the striatum of astrocyte-specific Rbp-J knockout (KO) mice glial fibrillary acidic protein (GFAP-CreER:Rbp-Jfl/fl), which may help to clarify detailed potential mechanisms for the post-ischemic neurogenesis in the striatum.

A Flexible Interdigital Electrode Used in Skin Penetration Promotion and Evaluation with Electroporation and Reverse Iontophoresis Synergistically.

Skin penetration is related to efficiencies of drug delivery or ISF extraction. Normally, the macro-electrode is employed in skin permeability promotion and evaluation, which has the disadvantages of easily causing skin damage when using electroporation or reverse iontophoresis by alone; furthermore, it has large measurement error, low sensitivity, and difficulty in integration. To resolve these issues, this paper presents a flexible interdigital microelectrode for evaluating skin penetration by sensing impedance and a method of synergistical combination of electroporation and reverse iontophoresis to promote skin penetration. First, a flexible interdigital microelectrode was designed with a minimal configuration circuit of electroporation and reverse iontophoresis for future wearable application. Due to the variation of the skin impedance correlated with many factors, relative changes of it were recorded at the end of supply, different voltage, or constant current, times, and duration. It is found that the better results can be obtained by using electroporation for 5 min then reverse iontophoresis for 12 min. By synergistically using electroporation and reverse iontophoresis, the penetration of skin is promoted. The results tested in vivo suggest that the developed microelectrode can be applied to evaluate and promote the skin penetration and the designed method promises to leave the skin without damage. The electrode and the method may be beneficial for designing noninvasive glucose sensors.

Review of Stratum Corneum Impedance Measurement in Non-Invasive Penetration Application.

Due to advances in telemedicine, mobile medical care, wearable health monitoring, and electronic skin, great efforts have been directed to non-invasive monitoring and treatment of disease. These processes generally involve disease detection from interstitial fluid (ISF) instead of blood, and transdermal drug delivery. However, the quantitative extraction of ISF and the level of drug absorption are greatly affected by the individual's skin permeability, which is closely related to the properties of the stratum corneum (SC). Therefore, measurement of SC impedance has been proposed as an appropriate way for assessing individual skin differences. In order to figure out the current status and research direction of human SC impedance detection, investigations regarding skin impedance measurement have been reviewed in this paper. Future directions are concluded after a review of impedance models, electrodes, measurement methods and systems, and their applications in treatment. It is believed that a well-matched skin impedance model and measurement method will be established for clinical and point-of care applications in the near future.