A Numerical Model of Blood Flow Velocity Measurement Based on Finger Ring

Fu, Yusheng; Wang, Qiyu; Yi, Jianbo; Duan, Song; Xiang, Xin

doi:10.1155/2018/3916481

Cited by 5 publications

(6 citation statements)

References 40 publications

(15 reference statements)

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“…Because of the large Verdet constant of the glucose molecule in comparison with molecules of other bloodstream components, we can assume that the interaction occurs primarily with the glucose dissolved in the plasma. 2,3,13,23,24 The magnetic rotatory power is commonly called Verdet's constant and is given as…”

Section: Speckle Pattern Analysesmentioning

confidence: 99%

“…Therefore, our path length is double. [24][25][26]28 The noise overlapping the Faraday effect is reduced using the AC magnetic field applied at a fixed frequency (we used 140 Hz), creating a magneto-optic effect on blood glucose. The magneto-optic effect produces a lock-in amplification that makes it possible to monitor blood glucose levels precisely.…”

Section: Speckle Pattern Analysesmentioning

confidence: 99%

“…Due to the low Verdet constant of most materials, weak magnetic fields only produce small rotations. Because of the large Verdet constant of the glucose molecule in comparison with molecules of other bloodstream components, we can assume that the interaction occurs primarily with the glucose dissolved in the plasma 2 , 3 , 13 , 23 , 24 …”

Section: Theoretical Backgroundmentioning

confidence: 99%

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Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses

Pal,

Kumar,

Avraham

et al. 2023

J. Biomed. Opt.

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Diabetes is a prevalent disease worldwide that can cause severe health problems. Accurate blood glucose detection is crucial for diabetes management, and noninvasive methods can be more convenient and less painful than traditional finger-prick methods. Aim:We aim to report a noncontact speckle-based blood glucose measurement system that utilizes artificial intelligence (AI) data processing to improve glucose detection accuracy. The study also explores the influence of an alternating current (AC) induced magnetic field on the sensitivity and selectivity of blood glucose detection. Approach:The proposed blood glucose sensor consists of a digital camera, an AC-generated magnetic field source, a laser illuminating the subject's finger, and a computer. A magnetic field is applied to the finger, and a camera records the speckle patterns generated by the laser light reflected from the finger. The acquired video data are preprocessed for machine learning (ML) and deep neural networks (DNNs) to classify blood plasma glucose levels. The standard finger-prick method is used as a reference for blood glucose level classification. Results:The study found that the noncontact speckle-based blood glucose measurement system with AI data processing allows for the detection of blood plasma glucose levels with high accuracy. The ML approach gives better results than the tested DNNs as the proposed data preprocessing is highly selective and efficient. Conclusions:The proposed noncontact blood glucose sensing mechanism utilizing AI data processing and a magnetic field can potentially improve glucose detection accuracy, making it more convenient and less painful for patients. The system also allows for inexpensive blood glucose sensing mechanisms and fast blood glucose screening. The results suggest that noninvasive methods can improve blood glucose detection accuracy, which can have significant implications for diabetes management. Investigations involving representative sampling data, including subjects of different ages, gender, race, and health status, could allow for further improvement.

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Section: Speckle Pattern Analysesmentioning

confidence: 99%

Section: Speckle Pattern Analysesmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

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Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses

Pal,

Kumar,

Avraham

et al. 2023

J. Biomed. Opt.

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“…One of the important applications of flow meters is to measure the flow velocity of the blood. Blood flow velocity measurement is important in medical applications for monitoring heart function in order to DRAFT arXiv:2007.02360v1 [cs.IT] 5 Jul 2020 diagnose cardiovascular or other vascular diseases [2]. Some traditional blood flow measurement methods are indicator method [3]- [5], finger plethysmography [6], and electromagnetic based method [7].…”

Section: Introductionmentioning

confidence: 99%

“…These methods have low resolution. Methods with higher resolution, based on the microfluidic technology, are ultrasonic doppler method and laser doppler method [2].…”

Section: Introductionmentioning

confidence: 99%

On Molecular Flow Velocity Meters

Farahnak-Ghazani

Mirmohseni

Nasiri-Kenari

2021

IEEE Trans. Mol. Biol. Multi-Scale Commun.

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The concentration of molecules in the medium can provide us very useful information about the medium. In this paper, we use this information and design a molecular flow velocity meter using a molecule releasing node and a receiver that counts these molecules. We first assume M hypotheses according to M possible medium flow velocity values and an L-sample decoder at the receiver and obtain the flow velocity detector using maximum-a-posteriori (MAP) method. To analyze the performance of the proposed flow velocity detector, we obtain the error probability, and its Gaussian approximation and Chernoff information (CI) upper bound. We obtain the optimum sampling times which minimize the error probability and the sub-optimum sampling times which minimize the Gaussian approximation and the CI upper bound. When we have binary hypothesis, we show that the sub-optimum sampling times which minimize the CI upper bound are equal. When we have M hypotheses and L → ∞, we show that the sub-optimum sampling times that minimize the CI upper bound yield to M 2 sampling times with M 2 weights. Then, we assume a randomly chosen constant flow velocity and obtain the MAP and minimum mean square error (MMSE) estimators for the L-sample receiver. We consider the mean square error (MSE) to investigate the error performance of the flow velocity estimators and obtain the Bayesian Cramer-Rao (BCR) and expected Cramer-Rao (ECR) lower bounds on the MSE of the estimators. Further, we obtain the sampling times which minimize the MSE. We show that when the flow velocity is in the direction of the connecting line between the releasing node and the receiver with uniform distribution for the magnitude of the flow velocity, and L → ∞, two different sampling times are enough for the MAP estimator.

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