scholarly journals Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6558
Author(s):  
Yang Chen ◽  
Chengcheng Hong ◽  
Michael R. Pinsky ◽  
Ting Ma ◽  
Gilles Clermont
Keyword(s):  
Blood Loss ◽  
Early Stage ◽  
Estimation Error ◽  
Estimation Method ◽  
Vital Signs ◽  
Estimation Methods ◽  
Clinical Implementation ◽  
Arterial Blood ◽  
Blood Loss Volume ◽  
Estimation Models

Background: There are currently no effective and accurate blood loss volume (BLV) estimation methods that can be implemented in operating rooms. To improve the accuracy and reliability of BLV estimation and facilitate clinical implementation, we propose a novel estimation method using continuously monitored photoplethysmography (PPG) and invasive arterial blood pressure (ABP). Methods: Forty anesthetized York Pigs (31.82 ± 3.52 kg) underwent a controlled hemorrhage at 20 mL/min until shock development was included. Machine-learning-based BLV estimation models were proposed and tested on normalized features derived by vital signs. Results: The results showed that the mean ± standard deviation (SD) for estimating BLV against the reference BLV of our proposed random-forest-derived BLV estimation models using PPG and ABP features, as well as the combination of ABP and PPG features, were 11.9 ± 156.2, 6.5 ± 161.5, and 7.0 ± 139.4 mL, respectively. Compared with traditional hematocrit computation formulas (estimation error: 102.1 ± 313.5 mL), our proposed models outperformed by nearly 200 mL in SD. Conclusion: This is the first attempt at predicting quantitative BLV from noninvasive measurements. Normalized PPG features are superior to ABP in accurately estimating early-stage BLV, and normalized invasive ABP features could enhance model performance in the event of a massive BLV.

scholarly journals Estimating fusion properties for functionalised acids

2011 ◽  
Vol 11 (16) ◽  
pp. 8385-8394 ◽  
Author(s):  
S. Compernolle ◽  
K. Ceulemans ◽  
J.-F. Müller
Keyword(s):  
Vapour Pressure ◽  
Estimation Error ◽  
Estimation Method ◽  
Organic Aerosol ◽  
Estimation Methods ◽  
Fusion Temperature ◽  
Subcooled Liquid ◽  
Fusion Data ◽  
Simple Estimation ◽  
Data Estimation

Abstract. Multicomponent organic aerosol (OA) is likely to be liquid, or partially liquid. Hence, to describe the partitioning of these components, their liquid vapour pressure is desired. Functionalised acids (e.g. diacids) can be a significant part of OA. But often measurements are available only for solid state vapour pressure, which can differ by orders of magnitude from their liquid counterparts. To convert such a sublimation pressure to a subcooled liquid vapour pressure, fusion properties (two out of these three quantities: fusion enthalpy, fusion entropy, fusion temperature) are required. Unfortunately, experimental knowledge of fusion properties is sometimes missing in part or completely, hence an estimation method is required. Several fusion data estimation methods are tested here against experimental data of functionalised acids, and a simple estimation method is developed, specifically for this family of compounds, with a significantly smaller estimation error than the literature methods.

scholarly journals Two-hop distance estimation in wireless sensor networks

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Sun Young Park ◽  
Daehoon Kwon ◽  
Jaehyun Ham ◽  
Young-Bae Ko ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Estimation Error ◽  
Estimation Method ◽  
Distance Estimation ◽  
Wireless Sensor ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Distance Information ◽  
Wide Range

In wireless sensor networks, the accurate estimation of distances between sensor nodes is essential. In addition to the distance information available for immediate neighbors within a sensing range, the distance estimation of two-hop neighbors can be exploited in various wireless sensor network applications such as sensor localization, robust data transfer against hidden terminals, and geographic greedy routing. In this article, we propose a two-hop distance estimation method, which first obtains the region in which the two-hop neighbor nodes possibly exist and then takes the average of the distances to the points in that region. The improvement in the estimation accuracy achieved by the proposed method is analyzed in comparison with a simple summation method that adds two single-hop distances as an estimate of a two-hop distance. Numerical simulation results show that in comparison with other existing distance estimation methods, the proposed method significantly reduces the distance estimation error over a wide range of node densities.

Lower Bound on the Accuracy of Parameter Estimation Methods for Linear Sensorimotor Synchronization Models

2015 ◽  
Vol 3 (1-2) ◽  
pp. 32-51 ◽  
Author(s):  
Nori Jacoby ◽  
Peter E. Keller ◽  
Bruno H. Repp ◽  
Merav Ahissar ◽  
Naftali Tishby
Keyword(s):  
Parameter Estimation ◽  
Lower Bound ◽  
Computational Models ◽  
Linear Models ◽  
Estimation Error ◽  
Explanatory Power ◽  
Estimation Method ◽  
Sensorimotor Synchronization ◽  
Estimation Methods ◽  
Unbiased Estimation

The mechanisms that support sensorimotor synchronization — that is, the temporal coordination of movement with an external rhythm — are often investigated using linear computational models. The main method used for estimating the parameters of this type of model was established in the seminal work of Vorberg and Schulze (2002), and is based on fitting the model to the observed auto-covariance function of asynchronies between movements and pacing events. Vorberg and Schulze also identified the problem of parameter interdependence, namely, that different sets of parameters might yield almost identical fits, and therefore the estimation method cannot determine the parameters uniquely. This problem results in a large estimation error and bias, thereby limiting the explanatory power of existing linear models of sensorimotor synchronization. We present a mathematical analysis of the parameter interdependence problem. By applying the Cramér–Rao lower bound, a general lower bound limiting the accuracy of any parameter estimation procedure, we prove that the mathematical structure of the linear models used in the literature determines that this problem cannot be resolved by any unbiased estimation method without adopting further assumptions. We then show that adding a simple and empirically justified constraint on the parameter space — assuming a relationship between the variances of the noise terms in the model — resolves the problem. In a follow-up paper in this volume, we present a novel estimation technique that uses this constraint in conjunction with matrix algebra to reliably estimate the parameters of almost all linear models used in the literature.

scholarly journals Evaluation of Evapotranspiration Estimation Models for Junagadh City of Gujarat

2016 ◽  
Vol 11 (2) ◽  
pp. 619-630
Author(s):  
H. H Mashru ◽  
D. K Dwivedi
Keyword(s):  
Performance Evaluation ◽  
Reference Evapotranspiration ◽  
Estimation Method ◽  
Irrigation Scheduling ◽  
Alternative Methods ◽  
Estimation Methods ◽  
Economic Returns ◽  
Field Crops ◽  
Evapotranspiration Estimation ◽  
Estimation Models

Estimation of Evapotranspiration is important for determining the agro-climatic potential of a particular region, water requirement of field crops, irrigation scheduling and suitability of crops or varieties, which can be grown successfully with the best economic returns and therefore numerous models have been developed for determining evapotranspiration. The performance evaluation of commonly used reference evapotranspiration (ET0) estimation methods like FAO 56 Penman-Monteith, Samani and Hargreaves, Makkink, Blaney Criddle, Jensen-Haise, Priestly-Taylor, FAO 24 radiation and Modified Penman Monteith method based on their accuracy of estimation has been undertaken in this study. The inter-relationship between FAO-56 Penman-Monteith method and other reference evapotranspiration (ET0) estimation method is also determined in this study. The results showed that Blaney Criddle method, Modified Penman method, Jensen-Haise method and Priestly-Taylor method are the alternative methods to Penman-Monteith method for better estimate of ET0 for the Junagadh city of Gujarat, India.

scholarly journals A Sparse-Based Off-Grid DOA Estimation Method for Coprime Arrays

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3025 ◽  
Author(s):  
Weijian Si ◽  
Fuhong Zeng ◽  
Changbo Hou ◽  
Zhanli Peng
Keyword(s):  
Covariance Matrix ◽  
Estimation Error ◽  
Estimation Method ◽  
Doa Estimation ◽  
Sample Covariance Matrix ◽  
Estimation Methods ◽  
Matrix Estimation ◽  
Estimation Process ◽  
Sample Covariance ◽  
Coprime Arrays

Recently, many sparse-based direction-of-arrival (DOA) estimation methods for coprime arrays have become popular for their excellent detection performance. However, these methods often suffer from grid mismatch problem due to the discretization of the potential angle space, which will cause DOA estimation performance degradation when the target is off-grid. To this end, we proposed a sparse-based off-grid DOA estimation method for coprime arrays in this paper, which includes two parts: coarse estimation process and fine estimation process. In the coarse estimation process, the grid points closest to the true DOAs, named coarse DOAs, are derived by solving an optimization problem, which is constructed according to the statistical property of the vectorized covariance matrix estimation error. Meanwhile, we eliminate the unknown noise variance effectively through a linear transformation. Due to finite snapshots effect, some undesirable correlation terms between signal and noise vectors exist in the sample covariance matrix. In the fine estimation process, we therefore remove the undesirable correlation terms from the sample covariance matrix first, and then utilize a two-step iterative method to update the grid biases. Combining the coarse DOAs with the grid biases, the final DOAs can be obtained. In the end, simulation results verify the effectiveness of the proposed method.

scholarly journals GSM and Arduino Based Vital Sign Monitoring System

2021 ◽  
Vol 15 (1) ◽  
pp. 78-89
Author(s):  
Rachel Austin ◽  
Fiona Lobo ◽  
Swarnalatha Rajaguru
Keyword(s):  
Vital Sign ◽  
Soft Tissues ◽  
Medical Information ◽  
Early Stage ◽  
Text Message ◽  
Vital Signs ◽  
Disease Diagnosis ◽  
Health Condition ◽  
Monitoring Method ◽  
Arterial Blood

Aims: Analysis of the vital signs of patients can aid in early disease diagnosis and care. There are many illnesses which can be diagnosed and managed by monitoring this medical information periodically. Background: Detection of various early-stage medical diseases can be simply done by monitoring Human vital signs, as they show the standard body's essential functions, indicating the status of an individual's health condition. Objective: In many cases it so happens that patients do not receive appropriate medical treatment on time, as a result of which unexpected incidents happen due to ignorance of one’s health status. Since pulse rate and vital sign area unit are the foremost crucial parameters, an affordable device to detect such parameters is useful for human health. Methods: Photoplethysmography (PPG) is a photosensitive technique that measures difference in blood volume of the pulse (usually in the body's soft tissues) by specifically interfering with the differences in the photo-emitter's absorption, reflection, volume and dispersion of light, and then it is registered by the photoreceptor. Then, the PPG waveform reflects shifts in arterial blood supply. The waveform obtained with a pulse oximeter illuminates skin and processes variations in the absorption of light. Results: The embedded systems-Arduino, GSM module and the various sensors used in this research provide a simple monitoring method which does not require a smartphone or internet connectivity. Conclusion: This paper aims at illustrating the significance of constantly monitoring vital signs. The device proposed has been developed as an Arduino program that is straightforward and inexpensive, a conveyable system that acquires the vital signs data and sends a text message as warning messages during a health emergency.

scholarly journals Technical Note: Estimating fusion properties for polyacids

2011 ◽  
Vol 11 (3) ◽  
pp. 7535-7553 ◽  
Author(s):  
S. Compernolle ◽  
K. Ceulemans ◽  
J.-F. Müller
Keyword(s):  
Vapor Pressure ◽  
Estimation Error ◽  
Estimation Method ◽  
Technical Note ◽  
Estimation Methods ◽  
Subcooled Liquid ◽  
Fusion Data ◽  
Simple Estimation ◽  
Data Estimation ◽  
Liquid Vapor

Abstract. Organic aerosol (OA) components are generally assumed to be liquid-like. Hence, to describe the partitioning of these components, the liquid vapor pressure of these components is desired. Polyacids and functionalized polyacids can be a significant part of OA. But often, measurements are available only for solid state vapor pressure, which can differ by orders of magnitude from their liquid counterparts. To convert such a sublimation pressure to a subcooled liquid vapor pressure, fusion properties (two out of these three quantities: fusion enthalpy, fusion entropy, fusion temperature) are required. Unfortunately, experimental knowledge of fusion properties is sometimes missing in part or totally, hence an estimation method is required. Several fusion data estimation methods are tested here against experimental data of polyacids. Next, we develop a simple estimation method, specifically for this kind of compounds, reducing significantly the estimation error.

scholarly journals Position and Attitude Estimation Method Integrating Visual Odometer and GPS

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2121 ◽  
Author(s):  
Yu Yang ◽  
Qiang Shen ◽  
Jie Li ◽  
Zilong Deng ◽  
Hanyu Wang ◽  
...  
Keyword(s):  
Estimation Error ◽  
Estimation Method ◽  
Attitude Estimation ◽  
Estimation Methods ◽  
Hardware In The Loop ◽  
Trajectory Estimation ◽  
Gps Positioning ◽  
Visual Odometer ◽  
State Of Art ◽  
Better Than

The monocular visual odometer is widely used in the navigation of robots and vehicles, but it has defects of the unknown scale of the estimated trajectory. In this paper, we presented a position and attitude estimation method, integrating the visual odometer and Global Position System (GPS), where the GPS positioning results were taken as a reference to minimize the trajectory estimation error of visual odometer and derive the attitude of the vehicle. Hardware-in-the-loop simulations were carried out; the experimental results showed that the positioning error of the proposed method was less than 1 m, and the accuracy and robustness of the attitude estimation results were better than those of the state-of-art vision-based attitude estimation methods.

scholarly journals A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1349 ◽  
Author(s):  
Qiaohua Fang ◽  
Xuezhe Wei ◽  
Tianyi Lu ◽  
Haifeng Dai ◽  
Jiangong Zhu
Keyword(s):  
Estimation Error ◽  
Estimation Method ◽  
Lithium Ion ◽  
The State ◽  
Estimation Methods ◽  
Model Parameters ◽  
Battery Management System ◽  
Relaxation Model ◽  
State Of Health ◽  
Capacity Estimation

The state of health estimation for lithium-ion battery is a key function of the battery management system. Unlike the traditional state of health estimation methods under dynamic conditions, the relaxation process is studied and utilized to estimate the state of health in this research. A reasonable and accurate voltage relaxation model is established based on the linear relationship between time coefficient and open circuit time for a Li1(NiCoAl)1O2-Li1(NiCoMn)1O2/graphite battery. The accuracy and effectiveness of the model is verified under different states of charge and states of health. Through systematic experiments under different states of charge and states of health, it is found that the model parameters monotonically increase with the aging of the battery. Three different capacity estimation methods are proposed based on the relationship between model parameters and residual capacity, namely the α-based, β-based, and parameter–fusion methods. The validation of the three methods is verified with high accuracy. The results indicate that the capacity estimation error under most of the aging states is less than 1%. The largest error drops from 3% under the α-based method to 1.8% under the parameter–fusion method.

scholarly journals GPS Sparse Multipath Signal Estimation Based on Compressive Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guodong He ◽  
Maozhong Song ◽  
Shanshan Zhang ◽  
Huiping Qin ◽  
Xiaojuan Xie
Keyword(s):  
Compressive Sensing ◽  
Estimation Error ◽  
Estimation Method ◽  
Reconstruction Algorithm ◽  
Estimation Methods ◽  
Signal Estimation ◽  
Reconstruction Algorithms ◽  
Modified Newton Method ◽  
Reconstruction Performance ◽  
Multipath Signal

A GPS sparse multipath signal estimation method based on compressive sensing is proposed. A new 0 norm approximation function is designed, and the parameter of the approximate function is gradually reduced to realize the approximation of 0 norm. The sparse signal is reconstructed by a modified Newton method. The reconstruction performance of the proposed algorithm is better than several commonly reconstruction algorithms at different sparse numbers and noise intensities. The GPS sparse multipath signal model is established, and the sparse multipath signal is estimated by the proposed reconstruction algorithm in this paper. Compared with several commonly used estimation methods, the estimation error of the proposed method is lower.

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