A portable high-density absolute-measure NIRS imager for detecting prefrontal lobe activity under fatigue driving

Zhao, Yue; Xu, Guoyi; Sun, Yunlong; Pan, Boan; Li, Ting

doi:10.1016/j.microrel.2018.02.002

Cited by 15 publications

(8 citation statements)

References 8 publications

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“…A device based on NIRS technology was developed by our research group. Similar NIR devices have been developed and proven to be reliable and stable during the performance evaluation . The NIR device consists of three modules: the NIR probe, the controlling and processing module and a data acquisition app, showed in Figure .…”

Section: Methodsmentioning

confidence: 99%

Monte Carlo modeling of photon migration in realistic human thoracic tissues for noninvasive monitoring of cardiac hemodynamics

Liu

Chen

Fang

et al. 2019

Journal of Biophotonics

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Noninvasive monitoring of cardiac hemodynamics remains challenging in cardiovascular medicine. The possibility of noninvasive optical monitoring of cardiac hemodynamics was theoretically investigated in this study. By utilizing the Monte Carlo simulation method for voxelized media (MCVM) and Visible Chinese Human dataset, we quantified and visualized the photon migration in human thoracic region. The light fluence distribution was showed to reach heart tissue (∼3 cm depth underbody surface) and 12% of the total fluence was absorbed by the myocardium. The proportion of spatial sensitivity distribution (SSD) in cardiac tissue to the total SSD reached 0.0195%. The portion of SSD increased following with cardiac diastole and diffuse reflectance deceased linearly with increasing cardiac volume. The optimal separation between the light source and detector was provided to be 3.5 to 4.0 cm for future development of noninvasive cardiac hemodynamics monitoring. A pilot experimental study was conducted to measure the diffuse reflectance light and fingertip photoplethysmography. These data suggest that the fluctuation period of near‐infrared (NIR) diffuse reflectance was consistent with the cardiac cycle, while the fluctuation features of the NIR signal was not consistent with that of photoplethysmography. All results indicate the great potential of noninvasive optical monitoring of myocardial hemodynamics.

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Section: Methodsmentioning

confidence: 99%

Monte Carlo modeling of photon migration in realistic human thoracic tissues for noninvasive monitoring of cardiac hemodynamics

Liu

Chen

Fang

et al. 2019

Journal of Biophotonics

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“…Unequivocally, it is more than coincidence that all three hemodynamic concentrations attained the same improved level of fit via the experiment on removal effect. We integrated the recommended function into the software of the medical NIRS instrument for stable online calibration in a series of successful clinical applications, including shock patient monitoring [ 28 ], blood thrombosis monitoring [ 36 ], and other clinical studies [ 29 , 31 ]. The calibrating algorithm online for every hemodynamic parameter will improve the accuracy of medical NIRS instruments.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…The functional module is the key part of the device. It includes a power circuit, control module, light source drive, data conversion module, display module, and transmission module [ 31 ]. The brief working process starts from the control module.…”

Section: Methodsmentioning

confidence: 99%

“…With such a characterized function, we were able to subtract baseline shift online and calibrate the hemodynamic measurements by NIRS accordingly. First, we used a noninvasive clinic-specific NIRS device developed in our lab to collect data with a solid phantom as the reference standard [ 27 , 28 , 29 , 30 , 31 ]. Then, the reference baseline was extracted and fitted with a series of mathematical functions provided in the MATLAB curve fitting toolbox.…”

Section: Introductionmentioning

confidence: 99%

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Online Removal of Baseline Shift with a Polynomial Function for Hemodynamic Monitoring Using Near-Infrared Spectroscopy

Zhao

et al. 2018

Sensors

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Near-infrared spectroscopy (NIRS) has become widely accepted as a valuable tool for noninvasively monitoring hemodynamics for clinical and diagnostic purposes. Baseline shift has attracted great attention in the field, but there has been little quantitative study on baseline removal. Here, we aimed to study the baseline characteristics of an in-house-built portable medical NIRS device over a long time (>3.5 h). We found that the measured baselines all formed perfect polynomial functions on phantom tests mimicking human bodies, which were identified by recent NIRS studies. More importantly, our study shows that the fourth-order polynomial function acted to distinguish performance with stable and low-computation-burden fitting calibration (R-square >0.99 for all probes) among second- to sixth-order polynomials, evaluated by the parameters R-square, sum of squares due to error, and residual. This study provides a straightforward, efficient, and quantitatively evaluated solution for online baseline removal for hemodynamic monitoring using NIRS devices.

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“…Hence, the driver's fatigue state detection becomes a hotspot in the driver sleepiness OF ROAD AND BRIDGE ENGINEERING 2 02 1 / 1 6 (1) research field. Vehicle fatigue pre-warning equipment is considered to be the most important tool for fatigued driving prevention (Zhao, Xu, Sun, Pan, & Li, 2018b), the automatic driving sleepiness discrimination algorithm being its core element.…”

Section: Introductionmentioning

confidence: 99%

Driver Sleepiness Detection Algorithm Based on Relevance Vector Machine

Wei

Feng

Zhao

et al. 2021

BJRBE

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Driver sleepiness is one of the most important causes of traffic accidents. Efficient and stable algorithms are crucial for distinguishing nonfatigue from fatigue state. Relevance vector machine (RVM) as a leading-edge detection approach allows meeting this requirement and represents a potential solution for fatigue state detection. To accurately and effectively identify the driver’s fatigue state and reduce the number of traffic accidents caused by driver sleepiness, this paper considers the degree of driver’s mouth opening and eye state as multi-source related variables and establishes classification of fatigue and non-fatigue states based on the related literature and investigation. On this basis, an RVM model for automatic detection of the fatigue state is proposed. Twenty male respondents participated in the data collection process and a total of 1000 datasets of driving status (half of non-fatigue and half of fatigue) were obtained. The results of fatigue state recognition were analysed by different RVM classifiers. The results show that the recognition accuracy of the RVM-driven state classifiers with different kernel functions was higher than 90%, which indicated that the mouth-opening degree and the eye state index used in this work were closely related to the fatigue state. Based on the obtained results, the proposed fatigue state identification method has the potential to improve the fatigue state detection accuracy. More importantly, it provides a scientific theoretical basis for the development of fatigue state warning methods.

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A portable high-density absolute-measure NIRS imager for detecting prefrontal lobe activity under fatigue driving

Cited by 15 publications

References 8 publications

Monte Carlo modeling of photon migration in realistic human thoracic tissues for noninvasive monitoring of cardiac hemodynamics

Monte Carlo modeling of photon migration in realistic human thoracic tissues for noninvasive monitoring of cardiac hemodynamics

Online Removal of Baseline Shift with a Polynomial Function for Hemodynamic Monitoring Using Near-Infrared Spectroscopy

Driver Sleepiness Detection Algorithm Based on Relevance Vector Machine

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