Pulse Rate Guided Oxygen Saturation Monitoring Using a Wearable Armband Sensor.

Continuous clinical grade measurement of SpO2 in out-of-hospital settings remains a challenge despite the widespread use of photoplethysmography (PPG) based wearable devices for health and wellness applications. This article presents two SpO2 algorithms: PRR (pulse rate derived ratio-of-ratios) and GPDR (green-assisted peak detection ratio-of-ratios), that utilize unique pulse rate frequency estimations to isolate the pulsatile (AC) component of red and infrared PPG signals and derive SpO2 measurements. The performance of the proposed SpO2 algorithms are evaluated using an upper-arm wearable device derived green, red, and infrared PPG signals, recorded in both controlled laboratory settings involving healthy subjects (n=36) and an uncontrolled clinic application involving COVID-19 patients (n=52). GPDR exhibits the lowest root mean square error (RMSE) of 1.6±0.6% for a respiratory exercise test, 3.6 ±1.0% for a standard hypoxia test, and 2.2±1.3% for an uncontrolled clinic use-case. In contrast, PRR provides relatively higher error but with greater coverage overall. Mean error across all combined datasets were 0.2±2.8% and 0.3±2.4% for PRR and GPDR respectively. Both SpO2 algorithms achieve great performance of low error with high coverage on both uncontrolled clinic and controlled laboratory conditions.

Breath analysis for detection and trajectory monitoring of acute respiratory distress syndrome in swine.

Despite the enormous impact on human health, acute respiratory distress syndrome (ARDS) is poorly defined, and its timely diagnosis is difficult, as is tracking the course of the syndrome. The objective of this pilot study was to explore the utility of breath collection and analysis methodologies to detect ARDS through changes in the volatile organic compound (VOC) profiles present in breath. Five male Yorkshire mix swine were studied and ARDS was induced using both direct and indirect lung injury. An automated portable gas chromatography device developed in-house was used for point of care breath analysis and to monitor swine breath hourly, starting from initiation of the experiment until the development of ARDS, which was adjudicated based on the Berlin criteria at the breath sampling points and confirmed by lung biopsy at the end of the experiment. A total of 67 breath samples (chromatograms) were collected and analysed. Through machine learning, principal component analysis and linear discrimination analysis, seven VOC biomarkers were identified that distinguished ARDS. These represent seven of the nine biomarkers found in our breath analysis study of human ARDS, corroborating our findings. We also demonstrated that breath analysis detects changes 1-6 h earlier than the clinical adjudication based on the Berlin criteria. The findings provide proof of concept that breath analysis can be used to identify early changes associated with ARDS pathogenesis in swine. Its clinical application could provide intensive care clinicians with a noninvasive diagnostic tool for early detection and continuous monitoring of ARDS.

Novel Continuous Respiratory Rate Monitoring Using an Armband Wearable Sensor.

Photoplethysmography (PPG) and accelerometer (ACC) are commonly integrated into wearable devices for continuous unobtrusive pulse rate and activity monitoring of individuals during daily life. However, obtaining continuous and clinically accurate respiratory rate measurements using such wearable sensors remains a challenge. This article presents a novel algorithm for estimation of respiration rate (RR) using an upper-arm worn wearable device by deriving multiple respiratory surrogate signals from PPG and ACC sensing. This RR algorithm is retrospectively evaluated on a controlled respiratory clinical testing dataset from 38 subjects with simultaneously recorded wearable sensor data and a standard capnography monitor as an RR reference. The proposed RR method shows great performance and robustness in determining RR measurements over a wide range of 4-59 brpm with an overall bias of -1.3 brpm, mean absolute error (MAE) of 2.7±1.6 brpm, and a meager outage of 0.3±1.2%, while a standard PPG Smart Fusion method produces a bias of -3.6 brpm, an MAE of 5.5±3.1 brpm, and an outage of 0.7±2.5% for direct comparison. In addition, the proposed algorithm showed no significant differences (p=0.63) in accurately determining RR values in subjects with darker skin tones, while the RR performance of the PPG Smart Fusion method is significantly (P<0.001) affected by the darker skin pigmentation. This study demonstrates a highly accurate RR algorithm for unobtrusive continuous RR monitoring using an armband wearable device.

Real Time Breath Analysis Using Portable Gas Chromatography for Adult Asthma Phenotypes.

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.

Heart Rate Monitoring using Sparse Spectral Curve Tracing.

Heart rate (HR) monitoring under real-world activities of daily living conditions is challenging, particularly, using peripheral wearable devices integrated with simple optical and acceleration sensors. The study presents a novel technique, named as CurToSS: CURve Tracing On Sparse Spectrum, for continuous HR estimation in daily living activity conditions using simultaneous photoplethysmogram (PPG) and triaxial-acceleration signals. The performance validation of HR estimation using the CurToSS algorithm is conducted in four public databases with distinctive study groups, sensor types, and protocols involving intense physical and emotional exertions. The HR performance of this time-frequency curve tracing method is also compared to that of contemporary algorithms. The results suggest that the CurToSS method offers the best performance with significantly (P<0.01) lowest HR error compared to spectral filtering and multi-channel PPG correlation methods. The current HR performances are also consistently better than a deep learning approach in diverse datasets. The proposed algorithm is powerful for reliable long-term HR monitoring under ambulatory daily life conditions using wearable biosensor devices.

Multiparameter urine analysis for quantitative bladder cancer surveillance of orthotopic xenografted mice.

The human-derived orthotopic xenograft mouse model is an effective platform for performing in vivo bladder cancer studies to examine tumor development, metastasis, and therapeutic effects of drugs. To date, the surveillance of tumor progression in real time for orthotopic bladder xenografts is highly dependent on semi-quantitative in vivo imaging technologies such as bioluminescence. While these imaging technologies can estimate tumor progression, they are burdened with requirements such as anesthetics, specialized equipment, and genetic modification of the injected cell line. Thus, a convenient and non-invasive technology to quantitatively monitor the growth of bladder cancer in orthotopic xenografts is highly desired. In this work, using a microfluidic chemiluminescent ELISA platform, we have successfully developed a rapid, multiparameter urine-based and non-invasive biomolecular prognostic technology for orthotopic bladder cancer xenografts. This method consists of two steps. First, the concentrations of a panel of four urinary biomarkers are quantified from the urine of mice bearing orthotopic bladder xenografts. Second, machine learning and principal component analysis (PCA) algorithms are applied to analyze the urinary biomarkers, and subsequently, a score is assigned to indicate the tumor growth. With this methodology, we have quantitatively monitored the orthotopic growth of human bladder cancer that was inoculated with low, medium, and high cancer cell numbers. We also employed this method and performed a proof of principle experiment to examine the in vivo therapeutic efficacy of the EGFR inhibitor, dacomitinib.

Peak focusing based on stationary phase thickness gradient.

This paper reports the development of a stationary phase thickness gradient gas chromatography (GC) column that enables analyte peak focusing and improves separation resolution. Theoretical analysis and simulation demonstrate focusing via a positive thickness gradient, i.e., the stationary phase thickness increases along the column. This effect was experimentally verified by coating a 5 m long capillary column with a film thickness varying from 34 nm at the column inlet to 241 nm at the column outlet. The column was analyzed in forward (thin to thick) and backward (thick to thin) modes and compared to a uniform thickness column with a thickness of 131 nm, using alkanes ranging from C5 to C16 and aromatics. Comparison of resolutions between forward mode and the uniform thickness column demonstrated an overall focusing rate (i.e., improvement in peak capacity) of 11.7% on alkanes and 28.2% on aromatics. The focusing effect was also demonstrated for isothermal room temperature separation of highly volatile compounds and temperature programmed separation with different ramping rates. In all cases, peak capacities from forward mode separations are higher than those from other modes, indicating the ability of a positive thickness gradient to focus analyte peaks. This thickness gradient technique can therefore be broadly applied to various stationary phases and column types as a general method for improving GC separation performance.

Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable two-dimensional gas chromatography device.

Acute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity. As a dynamic syndrome with multiple etiologies, its timely diagnosis is difficult as is tracking the course of the syndrome. Therefore, there is a significant need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Here, we report our work on using human breath to differentiate ARDS and non-ARDS causes of respiratory failure. A fully automated portable 2-dimensional gas chromatography device with high peak capacity (> 200 at the resolution of 1), high sensitivity (sub-ppb), and rapid analysis capability (~ 30 min) was designed and made in-house for on-site analysis of patients' breath. A total of 85 breath samples from 48 ARDS patients and controls were collected. Ninety-seven elution peaks were separated and detected in 13 min. An algorithm based on machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) was developed. As compared to the adjudications done by physicians based on the Berlin criteria, our device and algorithm achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value. The high overall accuracy and high positive predicative value suggest that the breath analysis method can accurately diagnose ARDS. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing practice and improving patient outcomes. Graphical abstract.

Rapid In Situ Analysis of Plant Emission for Disease Diagnosis Using a Portable Gas Chromatography Device.

We developed and applied a fully automated portable gas chromatography (GC) device for rapid and in situ analysis of plant volatile organic compounds (VOCs) to examine plant health status. A total of 42 emission samples were collected over a period of 5 days from 10 milkweed ( Asclepias syriaca) plants, half of which were infested by aphids. Thirty-five VOC peaks were separated and detected in 8 min. An algorithm based on machine learning, principal component analysis, and linear discriminant analysis was developed to evaluate the GC results. We found that our device and algorithm are able to distinguish between the undamaged control and the aphid-infested milkweeds with an overall accuracy of 90-100% within 48-72 h of the attack. Such rapid in situ detection of insect attack attests to the great potential of VOC monitoring in plant health management.

Fully Automated Portable Comprehensive 2-Dimensional Gas Chromatography Device.

We developed a fully automated portable 2-dimensional (2-D) gas chromatography (GC x GC) device, which had a dimension of 60 cm × 50 cm × 10 cm and weight less than 5 kg. The device incorporated a micropreconcentrator/injector, commercial columns, micro-Deans switches, microthermal injectors, microphotoionization detectors, data acquisition cards, and power supplies, as well as computer control and user interface. It employed multiple channels (4 channels) in the second dimension (2D) to increase the 2D separation time (up to 32 s) and hence 2D peak capacity. In addition, a nondestructive flow-through vapor detector was installed at the end of the 1D column to monitor the eluent from 1D and assist in reconstructing 1D elution peaks. With the information obtained jointly from the 1D and 2D detectors, 1D elution peaks could be reconstructed with significantly improved 1D resolution. In this Article, we first discuss the details of the system operating principle and the algorithm to reconstruct 1D elution peaks, followed by the description and characterization of each component. Finally, 2-D separation of 50 analytes, including alkane (C6-C12), alkene, alcohol, aldehyde, ketone, cycloalkane, and aromatic hydrocarbon, in 14 min is demonstrated, showing the peak capacity of 430-530 and the peak capacity production of 40-80/min.

Low-Power Miniaturized Helium Dielectric Barrier Discharge Photoionization Detectors for Highly Sensitive Vapor Detection.

This paper presents the design, fabrication, and characterization of a microhelium dielectric barrier discharge photoionization detector (µHDBD-PID) on chip with dimensions of only ∼15 mm × âˆ¼10 mm × âˆ¼0.7 mm and weight of only ∼0.25 g. It offers low power consumption (<400 mW), low helium consumption (5.8 mL/min), rapid response (up to ∼60 ms at a flow rate of 1.5 mL/min), quick warm-up time (∼5 min), an excellent detection limit (a few picograms), a large linear dynamic range (>4 orders of magnitude), and maintenance-free operation. Furthermore, the µHDBD-PID can be driven with a miniaturized (∼5 cm × âˆ¼2.5 cm × âˆ¼2.5 cm), light (22 g), and low cost (∼$2) power supply with only 1.5 VDC input. The dependence of the µHDBD-PID performance on bias voltage, auxiliary helium flow rate, carrier gas flow rate, and temperature was also systematically investigated. Finally, the µHDBD-PID was employed to detect permanent gases and a sublist of the EPA 8260 standard reagents that include 51 analytes. The µHDBD-PID developed here can have a broad range of applications in portable and microgas chromatography systems for in situ, real-time, and sensitive gas analysis.

In situ calibration of micro-photoionization detectors in a multi-dimensional micro-gas chromatography system.

A photoionization detector (PID) is widely used as a gas chromatography (GC) detector. By virtue of its non-destructive nature, multiple PIDs can be used in multi-dimensional GC. However, different PIDs have different responsivities towards the same chemical compound with the same concentration or mass due to different aging conditions of the PID lamps and windows. Here, we carried out a systematic study regarding the response of 5 Krypton µPIDs in a 1 × 4-channel 2-dimensional µGC system to 7 different volatile organic compounds (VOCs) with the ionization potential ranging from 8.45 eV to 10.08 eV and the concentration ranging from ∼1 ng to ∼2000 ng. We used one of the PIDs as the reference detector and calculated the calibration factor for each of the remaining 4 PIDs against the first PID, which we found is quite uniform regardless of the analyte, its concentration, or chromatographic peak width. Based on the above observation, we were able to quantitatively reconstruct the coeluted peaks in the first dimension using the signal obtained with a PID array in the second dimension. Our work will enable rapid and in situ calibration of PIDs in a GC system using a single analyte at a single concentration. It will also lead to the development of multi-channel multi-dimensional GC where multiple PIDs are employed.

Analysis and simulation of entirely saturated unilateral laser spot tails in BCCD.

A new phenomenon involving the entire saturation of unilateral tails of buried channel charge-coupled devices (BCCDs) under laser radiation is observed in this study. A physical model related to this phenomenon is constructed based on the assumption that the charge transfer inefficiency of BCCD is a jump function of signal charge quantity. The profile of a spot tail under laser radiation is simulated using this self-developed model. The simulation results are compared with experimental findings to validate this model.

Flow-through microfluidic photoionization detectors for rapid and highly sensitive vapor detection.

A photoionization detector (PID) is well known for its high sensitivity, large dynamic range, and non-destructive vapor detection capability. However, due to its tardy response, which results from the relatively large ionization chamber and dead volume, the application of the PID in gas chromatography (GC) has been limited. Here, we developed a rapid, flow-through, and highly sensitive microfluidic PID that was microfabricated directly on a conductive silicon wafer. The microfluidic PID has a significantly reduced ionization chamber volume of only 1.3 µL, nearly 10 times smaller than that of state-of-the-art PIDs and over 100 times smaller than that of commercial PIDs. Moreover, it has virtually zero dead volume due to its flow-through design. Consequently, the response time of the microfluidic PID can be considerably shortened, ultimately limited by its residence time (7.8 ms for 10 mL min(-1) and 78 ms for 1 mL min(-1)). Experimentally, the response of the microfluidic PID was measured to be the same as that of the standard flame ionization detector with peak full-widths-at-half-maximum of 0.25 s and 0.085 s for flow rates of 2.3 mL min(-1) and 10 mL min(-1), respectively. Our studies further show that the microfluidic PID was able to detect analytes down to the picogram level (at 3σ of noise) and had a linear dynamic range of six orders of magnitude. Finally, because of the very short distance between the electrodes, low voltage (<10 VDC, over 10 times lower than that in a regular PID) can be used for microfluidic PID operation. This work will open a door to broad applications of PIDs in gas analyzers, in particular, micro-GC and multi-dimensional GC.