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Cuffless blood pressure monitoring from a wristband with calibration-free algorithms for sensing location based on bio-impedance sensor array and autoencoder.

ABSTRACT: Continuous monitoring of blood pressure (BP) is essential for the prediction and the prevention of cardiovascular diseases. Cuffless BP methods based on non-invasive sensors integrated into wearable devices can translate blood pulsatile activity into continuous BP data. However, local blood pulsatile sensors from wearable devices suffer from inaccurate pulsatile activity measurement based on superficial capillaries, large form-factor devices and BP variation with sensor location which degrade the accuracy of BP estimation and the device wearability. This study presents a cuffless BP monitoring method based on a novel bio-impedance (Bio-Z) sensor array built in a flexible wristband with small-form factor that provides a robust blood pulsatile sensing and BP estimation without calibration methods for the sensing location. We use a convolutional neural network (CNN) autoencoder that reconstructs an accurate estimate of the arterial pulse signal independent of sensing location from a group of six Bio-Z sensors within the sensor array. We rely on an Adaptive Boosting regression model which maps the features of the estimated arterial pulse signal to systolic and diastolic BP readings. BP was accurately estimated with average error and correlation coefficient of 0.5 ± 5.0 mmHg and 0.80 for diastolic BP, and 0.2 ± 6.5 mmHg and 0.79 for systolic BP, respectively.

SUBMITTER: Ibrahim B

PROVIDER: S-EPMC8748973 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Calibration array

Project description:Calibration array

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Framework for the Simulation of Sensor Networks Aimed at Evaluating In Situ Calibration Algorithms.

Project description:The drastically increasing availability of low-cost sensors for environmental monitoring has fostered a large interest in the literature. One particular challenge for such devices is the fast degradation over time of the quality of their data. Therefore, the instruments require frequent calibrations. Traditionally, this operation is carried out on each sensor in dedicated laboratories. This is not economically sustainable for dense networks of low-cost sensors. An alternative that has been investigated is in situ calibration: exploiting the properties of the sensor network, the instruments are calibrated while staying in the field and preferably without any physical intervention. The literature indicates there is wide variety of in situ calibration strategies depending on the type of sensor network deployed. However, there is a lack for a systematic benchmark of calibration algorithms. In this paper, we propose the first framework for the simulation of sensor networks enabling a systematic comparison of in situ calibration strategies with reproducibility, and scalability. We showcase it on a primary test case applied to several calibration strategies for blind and static sensor networks. The performances of calibration are shown to be tightly related to the deployment of the network itself, the parameters of the algorithm and the metrics used to evaluate the results. We study the impact of the main modelling choices and adjustments of parameters in our framework and highlight their influence on the results of the calibration algorithms. We also show how our framework can be used as a tool for the design of a network of low-cost sensors.

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Calibration array

Project description:Calibration array used to assess probe specific binding behaviour across eight different amounts of DNA starting material. This calibration step was performed to select one best out of three probes per gene based on probe responsiveness on increasing DNA staring material. It was further used to calibrate a subsequent microarray experiment to account for probe specific binding behaviour as part of the normalization process (see also Dennenmoser et al. 2017 Copy number increases of transposable elements and protein coding genes in an invasive fish of hybrid origin).

2017-07-01 | GSE93063 | GEO

Bio-inspired polymer array vapor sensor with dual signals of fluorescence intensity and wavelength shift.

Project description:Organic vapor sensors based on polymer owing to their tunable molecular structures and designable functions have attracted considerable research interest. However, detecting multiple organic vapors with high accuracy and a low detection limit is still challenging. Herein, inspired by the mammalian olfactory recognition system, organic vapor sensors based on one-dimensional microfilament array structures with a wide range of sensing gases are demonstrated. By introducing aggregation-induced emission (AIE) molecules, sensors possess dual-optical sensing mechanisms of variation in fluorescence intensity and wavelength. By virtue of the synergistic effects of dual signals, superb accuracy and incredibly low detection limit are achieved for identifying analytes. In particular, the polymer/AIE microfilament array can detect acetone vapor down to 0.03% of saturated vapor pressure. In the saturated vapor of acetone, the fluorescence intensity of the sensor arrays was reduced by 53.7%, while the fluorescence wavelength was red-shifted by 21 nm. Combined with the principal component analysis (PCA) algorithm, the polymer/AIE molecular sensor arrays accomplished the classification and identification of acetone, ethanol, methylene chloride, toluene, and benzene. This bioinspired approach with dual sensing signals may broaden practical applications to high-performance gas sensors for precise molecular detection.

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Machine Learning for Light Sensor Calibration.

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A Sensor Array Using Multi-functional Field-effect Transistors with Ultrahigh Sensitivity and Precision for Bio-monitoring.

Project description:Mechanically adaptive electronic skins (e-skins) emulate tactition and thermoception by cutaneous mechanoreceptors and thermoreceptors in human skin, respectively. When exposed to multiple stimuli including mechanical and thermal stimuli, discerning and quantifying precise sensing signals from sensors embedded in e-skins are critical. In addition, different detection modes for mechanical stimuli, rapidly adapting (RA) and slowly adapting (SA) mechanoreceptors in human skin are simultaneously required. Herein, we demonstrate the fabrication of a highly sensitive, pressure-responsive organic field-effect transistor (OFET) array enabling both RA- and SA- mode detection by adopting easily deformable, mechano-electrically coupled, microstructured ferroelectric gate dielectrics and an organic semiconductor channel. We also demonstrate that the OFET array can separate out thermal stimuli for thermoreception during quantification of SA-type static pressure, by decoupling the input signals of pressure and temperature. Specifically, we adopt piezoelectric-pyroelectric coupling of highly crystalline, microstructured poly(vinylidene fluoride-trifluoroethylene) gate dielectric in OFETs with stimuli to allow monitoring of RA- and SA-mode responses to dynamic and static forcing conditions, respectively. This approach enables us to apply the sensor array to e-skins for bio-monitoring of humans and robotics.

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Fabrication and evaluation of a micro(bio)sensor array chip for multiple parallel measurements of important cell biomarkers.

Project description:This report describes the design and development of an integrated electrochemical cell culture monitoring system, based on enzyme-biosensors and chemical sensors, for monitoring indicators of mammalian cell metabolic status. MEMS technology was used to fabricate a microwell-format silicon platform including a thermometer, onto which chemical sensors (pH, O2) and screen-printed biosensors (glucose, lactate), were grafted/deposited. Microwells were formed over the fabricated sensors to give 5-well sensor strips which were interfaced with a multipotentiostat via a bespoke connector box interface. The operation of each sensor/biosensor type was examined individually, and examples of operating devices in five microwells in parallel, in either potentiometric (pH sensing) or amperometric (glucose biosensing) mode are shown. The performance characteristics of the sensors/biosensors indicate that the system could readily be applied to cell culture/toxicity studies.

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Gas Sensor Array Using a Hybrid Structure Based on Zeolite and Oxide Semiconductors for Multiple Bio-Gas Detection.

Project description:Semiconductor-type gas sensors, composed of metal-oxide semiconductors and porous zeolite materials, are attractive devices for bio-gas detection, particularly when used as bio-gas sensors such as electronic nose application. Previous studies have shown such detection can be obtained with a separate gas concentrator and a sensor device using zeolites and oxide semiconductors of WO3 nanoparticles. By applying the gas concentrator, porous molecular structures alter both the gas sensitivity and the selectivity, and even can be used to define the sensor characteristics. Based on such a gas sensor design, we investigated the properties of an array of three sensors made of a layer of WO3 nanoparticles coated with zeolites with different interactions between gas molecule adsorption and desorption. The array was tested with four volatile organic compounds, each measured at different concentrations. The results confirm that the features of individual zeolites combined with the hybrid gas sensor behavior, along with the differences among the sensors, are sufficient for enabling the discrimination of volatile compounds when disregarding their concentration.

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A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases.

Project description:Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficient knowledge, many patients are not diagnosed. Nowadays, the advances in high-throughput sequencing technologies such as whole genome sequencing, single-cell and others, have boosted the understanding of RDs. To extract biological meaning using the data generated by these methods, different analysis techniques have been proposed, including machine learning algorithms. These methods have recently proven to be valuable in the medical field. Among such approaches, unsupervised learning methods via neural networks including autoencoders (AEs) or variational autoencoders (VAEs) have shown promising performances with applications on various type of data and in different contexts, from cancer to healthy patient tissues. In this review, we discuss how AEs and VAEs have been used in biomedical settings. Specifically, we discuss their current applications and the improvements achieved in diagnostic and survival of patients. We focus on the applications in the field of RDs, and we discuss how the employment of AEs and VAEs would enhance RD understanding and diagnosis.

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Mobile road weather sensor calibration by sensor fusion and linear mixed models.

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