Towards Noninvasive Monitoring of Total Hemoglobin Concentration and Fractional Oxygen Saturation Based on Earlobe Pulse Oximetry

Abdallah, Omar; Alam, Kawther Abo; Bolz, A.

doi:10.1007/978-3-540-89208-3_414

Cited by 4 publications

(7 citation statements)

References 7 publications

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“…We recorded participants’ faces using cameras that are sensitive to RGB, NIR and LWIR spectra as participants watched videos that reliably evoked various emotions (fear, disgust, sexual arousal, amusement, or neutral) and reported their emotional state. Since the different examined wavelengths have different physical properties, such as skin penetration depth, hemoglobin absorption and so on 29 , 30 , TSTMS features were designed to maximize the physiological information captured by the cameras, based on remote photoplethysmography (rPPG) methods 31 , 32 . This high-dimensional dataset was used as inputs to a machine learning emotion classification algorithm that has found unique patterns associated with each emotion class.…”

Section: Discussionmentioning

confidence: 99%

“…A change in the medium traveled distance (Eq. 4 ) due to heart pulsation-induced arterial volume change 18 , 31 , 32 will be related to a change in the diffuse reflected light (Eq. 6 ).…”

Section: Data Processingmentioning

confidence: 99%

“…6 ). Since and are assumed to be constant, where occurs when the artery diameter is the greatest, and when artery diameter is the least 31 (maximum absorbance will yield minimum reflectance and vice versa), Eq. ( 6 ) can be derived with respect to temporal heart pulsation-induced changes, to become Eq.…”

Section: Data Processingmentioning

confidence: 99%

“… : Pulsatile amplitude. : Based on a measure of absorption that eliminates the effect of the tissue 20 , 21 , 31 , 43 . : Based on pulsatile amplitudes dissimilarity between two wavelengths, and 20 – 22 .…”

Section: Data Processingmentioning

confidence: 99%

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Classification of emotional states via transdermal cardiovascular spatiotemporal facial patterns using multispectral face videos

Shvimmer

Simhon

Michael

et al. 2022

Sci Rep

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We describe a new method for remote emotional state assessment using multispectral face videos, and present our findings: unique transdermal, cardiovascular and spatiotemporal facial patterns associated with different emotional states. The method does not rely on stereotypical facial expressions but utilizes different wavelength sensitivities (visible spectrum, near-infrared, and long-wave infrared) to gauge correlates of autonomic nervous system activity spatially and temporally distributed across the human face (e.g., blood flow, hemoglobin concentration, and temperature). We conducted an experiment where 110 participants viewed 150 short emotion-eliciting videos and reported their emotional experience, while three cameras recorded facial videos with multiple wavelengths. Spatiotemporal multispectral features from the multispectral videos were used as inputs to a machine learning model that was able to classify participants’ emotional state (i.e., amusement, disgust, fear, sexual arousal, or no emotion) with satisfactory results (average ROC AUC score of 0.75), while providing feature importance analysis that allows the examination of facial occurrences per emotional state. We discuss findings concerning the different spatiotemporal patterns associated with different emotional states as well as the different advantages of the current method over existing approaches to emotion detection.

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Section: Discussionmentioning

confidence: 99%

“…A change in the medium traveled distance (Eq. 4 ) due to heart pulsation-induced arterial volume change 18 , 31 , 32 will be related to a change in the diffuse reflected light (Eq. 6 ).…”

Section: Data Processingmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

Section: Data Processingmentioning

confidence: 99%

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Classification of emotional states via transdermal cardiovascular spatiotemporal facial patterns using multispectral face videos

Shvimmer

Simhon

Michael

et al. 2022

Sci Rep

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“…9 The other methods for Hgb measurement include electrochemistry, 8,10-12 spectrophotometry, 6,7,13,14 uorimetry, 1,5 chemiluminescence, 15 high performance liquid chromatography [16][17][18] and noninvasive methods. [19][20][21][22][23] Various types of nanoparticles have been applied for the measurement of Hgb. 1,3,4 Wang et al investigated the use of N-acetyl-L-cysteine (NAC) capped CdHgSe quantum dot (QD) for determination of Hgb.…”

Section: Introductionmentioning

confidence: 99%

Hemoglobin detection using curcumin nanoparticles as a colorimetric chemosensor

Pourreza

Golmohammadi

2015

RSC Adv.

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This article presents a simple and efficient measurement system for quantitative sensing of blood hemoglobin (Hgb) using curcumin nanoparticle (CURNs). The Hgb monitoring is based on CURNs aggregation in the presence of Hgb, which leads to a decrease in the absorption intensity of CURNs. In this study the analytical signal for the measurement of blood Hgb concentration was defined as the difference in absorption intensity of the CURNs in the absence and presence of Hgb. The method was optimized to enhance the selectivity and sensitivity of the method by investigating the effect of variables such as pH of the sample solution, buffer concentration, required amount of CURNs, equilibrium time and tolerance limit of various probable interferences. Under optimum conditions, the difference in absorption intensity of the CURNs was linearly proportional to the concentration of Hgb in two ranges of 1-40 µg mL -1 and 150-1200 µg mL -1 with a detection limit of 0.1 µg mL -1 . The relative standard deviation for ten replicate measurements of 20 µg mL -1 and 600 µg mL -1 of Hgb was 3.6% and 3.1% respectively.The sensing method was successfully applied for the measurement of Hgb in human blood with satisfactory results.

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